AI-Robot Brain Architecture: Integrating Perception, Planning, Control, and Learning
Welcome to Chapter 6 of Module 3: The AI-Robot Brain! This concluding chapter of the NVIDIA Isaac module brings together all the AI components we've explored - perception, planning, control, and learning - into a unified cognitive architecture for humanoid robots. We'll design and implement an integrated AI-Robot Brain that orchestrates these systems to enable sophisticated autonomous behavior.
Learning Objectives
By the end of this chapter, you will be able to:
- Design a unified cognitive architecture for humanoid robots
- Integrate perception, planning, control, and learning systems into a cohesive brain
- Implement real-time scheduling and task orchestration for AI components
- Create modular and extensible AI system architectures
- Design attention mechanisms and resource allocation strategies
- Implement learning and adaptation within the cognitive architecture
- Handle system failures and maintain robust operation
- Optimize the AI-Robot Brain for NVIDIA hardware acceleration
- Validate and test integrated cognitive architectures
Introduction to AI-Robot Brain Architecture
The Need for Unified Cognitive Architecture
Humanoid robots require sophisticated cognitive capabilities that integrate multiple AI systems working in harmony. Unlike simple task-specific robots, humanoid robots must handle complex, dynamic environments while performing diverse tasks that require perception, reasoning, planning, and learning. The AI-Robot Brain architecture provides a unified framework for orchestrating these capabilities.
Cognitive Architecture Principles
A well-designed AI-Robot Brain should embody the following principles:
- Modularity: Clear separation of concerns between different cognitive modules
- Real-time Performance: Guaranteed timing for safety-critical operations
- Scalability: Ability to add new capabilities without architectural changes
- Robustness: Graceful degradation when components fail
- Learning: Continuous adaptation and improvement of capabilities
- Attention: Resource allocation based on task importance and urgency
High-Level Architecture Overview
import asyncio
import threading
import queue
import time
from typing import Dict, List, Any, Optional, Callable
from dataclasses import dataclass
from enum import Enum
import numpy as np
class CognitiveModuleType(Enum):
"""Types of cognitive modules in the AI-Robot Brain"""
PERCEPTION = "perception"
PLANNING = "planning"
CONTROL = "control"
LEARNING = "learning"
MEMORY = "memory"
REASONING = "reasoning"
COMMUNICATION = "communication"
@dataclass
class CognitiveTask:
"""Represents a cognitive task in the AI-Robot Brain"""
id: str
module_type: CognitiveModuleType
priority: int # 0-10, higher is more urgent
deadline: float # Absolute time deadline
data: Dict[str, Any]
callback: Optional[Callable] = None
@dataclass
class CognitiveState:
"""Current state of the AI-Robot Brain"""
timestamp: float
perception_state: Dict[str, Any]
planning_state: Dict[str, Any]
control_state: Dict[str, Any]
learning_state: Dict[str, Any]
attention_state: Dict[str, Any]
class AIBrainCore:
"""Core of the AI-Robot Brain - handles task scheduling and orchestration"""
def __init__(self):
self.modules: Dict[CognitiveModuleType, Any] = {}
self.task_queue = queue.PriorityQueue()
self.running = False
self.main_loop_thread = None
self.attention_manager = AttentionManager()
self.resource_allocator = ResourceManager()
self.cognitive_state = CognitiveState(
timestamp=time.time(),
perception_state={},
planning_state={},
control_state={},
learning_state={},
attention_state={}
)
def register_module(self, module_type: CognitiveModuleType, module: Any):
"""Register a cognitive module with the brain"""
self.modules[module_type] = module
def schedule_task(self, task: CognitiveTask):
"""Schedule a cognitive task for execution"""
# Priority queue: higher priority tasks execute first
priority_score = -task.priority # Negative for max-heap behavior
self.task_queue.put((priority_score, time.time(), task))
def execute_task(self, task: CognitiveTask) -> Any:
"""Execute a cognitive task using the appropriate module"""
if task.module_type not in self.modules:
raise ValueError(f"Module {task.module_type} not registered")
module = self.modules[task.module_type]
# Execute the task
result = module.process(task.data)
# Update cognitive state
self.update_cognitive_state(task.module_type, result)
# Call callback if provided
if task.callback:
task.callback(result)
return result
def update_cognitive_state(self, module_type: CognitiveModuleType, result: Any):
"""Update the cognitive state based on module output"""
self.cognitive_state.timestamp = time.time()
if module_type == CognitiveModuleType.PERCEPTION:
self.cognitive_state.perception_state.update(result)
elif module_type == CognitiveModuleType.PLANNING:
self.cognitive_state.planning_state.update(result)
elif module_type == CognitiveModuleType.CONTROL:
self.cognitive_state.control_state.update(result)
elif module_type == CognitiveModuleType.LEARNING:
self.cognitive_state.learning_state.update(result)
def main_loop(self):
"""Main cognitive loop - executes tasks and manages attention"""
while self.running:
try:
# Get highest priority task
if not self.task_queue.empty():
_, _, task = self.task_queue.get_nowait()
# Check if task is still valid (not expired)
if time.time() <= task.deadline:
self.execute_task(task)
else:
print(f"Task {task.id} expired")
# Update attention and resource allocation
self.attention_manager.update(self.cognitive_state)
self.resource_allocator.update(self.cognitive_state)
# Sleep briefly to prevent busy waiting
time.sleep(0.001) # 1ms
except queue.Empty:
# No tasks to process, sleep longer
time.sleep(0.01) # 10ms
def start(self):
"""Start the AI-Robot Brain"""
self.running = True
self.main_loop_thread = threading.Thread(target=self.main_loop)
self.main_loop_thread.start()
def stop(self):
"""Stop the AI-Robot Brain"""
self.running = False
if self.main_loop_thread:
self.main_loop_thread.join()
class AttentionManager:
"""Manages attention allocation across cognitive modules"""
def __init__(self):
self.attention_weights = {}
self.stimuli_history = []
def update(self, cognitive_state: CognitiveState):
"""Update attention allocation based on current state"""
# Calculate attention weights based on urgency and importance
self.attention_weights = self.calculate_attention_weights(cognitive_state)
# Update stimuli history for learning
self.stimuli_history.append({
'timestamp': cognitive_state.timestamp,
'state': {
'perception': cognitive_state.perception_state,
'planning': cognitive_state.planning_state,
'control': cognitive_state.control_state
}
})
def calculate_attention_weights(self, cognitive_state: CognitiveState) -> Dict[CognitiveModuleType, float]:
"""Calculate attention weights for each module"""
weights = {}
# Perception attention based on environmental changes
perception_attention = self.calculate_perception_attention(cognitive_state)
weights[CognitiveModuleType.PERCEPTION] = perception_attention
# Planning attention based on goal urgency and plan validity
planning_attention = self.calculate_planning_attention(cognitive_state)
weights[CognitiveModuleType.PLANNING] = planning_attention
# Control attention based on safety and stability
control_attention = self.calculate_control_attention(cognitive_state)
weights[CognitiveModuleType.CONTROL] = control_attention
# Learning attention based on novelty and learning opportunities
learning_attention = self.calculate_learning_attention(cognitive_state)
weights[CognitiveModuleType.LEARNING] = learning_attention
return weights
def calculate_perception_attention(self, cognitive_state: CognitiveState) -> float:
"""Calculate attention weight for perception module"""
# Higher attention when environment is changing rapidly
recent_changes = cognitive_state.perception_state.get('change_rate', 0.0)
return min(1.0, recent_changes * 2.0) # Scale and cap at 1.0
def calculate_planning_attention(self, cognitive_state: CognitiveState) -> float:
"""Calculate attention weight for planning module"""
# Higher attention when goals are urgent or plans are invalid
goal_urgency = cognitive_state.planning_state.get('goal_urgency', 0.0)
plan_validity = cognitive_state.planning_state.get('plan_validity', 1.0)
# Inverse of plan validity means more attention when plan is poor
return max(goal_urgency, 1.0 - plan_validity)
def calculate_control_attention(self, cognitive_state: CognitiveState) -> float:
"""Calculate attention weight for control module"""
# Higher attention for safety-critical control
stability = cognitive_state.control_state.get('stability', 1.0)
safety_critical = cognitive_state.control_state.get('safety_critical', False)
control_attention = 1.0 - stability # More attention when unstable
if safety_critical:
control_attention = max(control_attention, 0.9) # High priority for safety
return min(1.0, control_attention)
def calculate_learning_attention(self, cognitive_state: CognitiveState) -> float:
"""Calculate attention weight for learning module"""
# Higher attention when encountering novel situations
novelty = cognitive_state.learning_state.get('novelty_score', 0.0)
learning_opportunity = cognitive_state.learning_state.get('learning_opportunity', 0.0)
return max(novelty, learning_opportunity)
class ResourceManager:
"""Manages computational resources across cognitive modules"""
def __init__(self):
self.resource_allocation = {}
self.resource_limits = {
'cpu': 1.0, # 100% of CPU
'gpu': 1.0, # 100% of GPU
'memory': 1.0, # 100% of memory
'bandwidth': 1.0 # 100% of bandwidth
}
def update(self, cognitive_state: CognitiveState):
"""Update resource allocation based on current needs"""
attention_weights = cognitive_state.attention_state
self.resource_allocation = self.allocate_resources(attention_weights)
def allocate_resources(self, attention_weights: Dict[CognitiveModuleType, float]) -> Dict[CognitiveModuleType, Dict[str, float]]:
"""Allocate computational resources based on attention weights"""
allocation = {}
# Normalize attention weights
total_attention = sum(attention_weights.values())
if total_attention == 0:
# Equal allocation if no attention weights
equal_weight = 1.0 / len(CognitiveModuleType)
for module_type in CognitiveModuleType:
allocation[module_type] = {
'cpu': equal_weight,
'gpu': equal_weight,
'memory': equal_weight,
'bandwidth': equal_weight
}
else:
# Allocate proportionally to attention weights
for module_type, weight in attention_weights.items():
normalized_weight = weight / total_attention
allocation[module_type] = {
'cpu': normalized_weight,
'gpu': normalized_weight,
'memory': normalized_weight,
'bandwidth': normalized_weight
}
return allocation
def get_resource_for_module(self, module_type: CognitiveModuleType, resource_type: str) -> float:
"""Get allocated resource amount for a specific module"""
if module_type in self.resource_allocation:
return self.resource_allocation[module_type].get(resource_type, 0.0)
return 0.0
Perception Integration
Multi-Modal Perception System
The perception module integrates multiple sensory inputs to create a comprehensive understanding of the environment. This includes visual, auditory, tactile, and proprioceptive information.
class PerceptionModule:
"""Multi-modal perception system for the AI-Robot Brain"""
def __init__(self):
self.visual_processor = VisualPerception()
self.auditory_processor = AuditoryPerception()
self.tactile_processor = TactilePerception()
self.proprioceptive_processor = ProprioceptivePerception()
# Integration components
self.sensor_fusion = SensorFusion()
self.object_detector = ObjectDetection()
self.scene_understanding = SceneUnderstanding()
def process(self, sensor_data: Dict[str, Any]) -> Dict[str, Any]:
"""Process multi-modal sensor data"""
# Process individual modalities
visual_results = self.visual_processor.process(
sensor_data.get('camera', {}),
sensor_data.get('depth', {})
)
auditory_results = self.auditory_processor.process(
sensor_data.get('microphone', {})
)
tactile_results = self.tactile_processor.process(
sensor_data.get('tactile', {})
)
proprioceptive_results = self.proprioceptive_processor.process(
sensor_data.get('joint_states', {}),
sensor_data.get('imu', {})
)
# Fuse sensor data
fused_perception = self.sensor_fusion.fuse({
'visual': visual_results,
'auditory': auditory_results,
'tactile': tactile_results,
'proprioceptive': proprioceptive_results
})
# Detect objects and understand scene
objects = self.object_detector.detect(fused_perception)
scene_description = self.scene_understanding.understand(
fused_perception, objects
)
# Update internal state
self.update_internal_state(fused_perception, objects, scene_description)
return {
'fused_perception': fused_perception,
'detected_objects': objects,
'scene_description': scene_description,
'change_rate': self.calculate_change_rate(fused_perception),
'attention_targets': self.identify_attention_targets(objects)
}
def update_internal_state(self, perception: Dict, objects: List, scene: Dict):
"""Update internal perception state"""
pass
def calculate_change_rate(self, perception: Dict) -> float:
"""Calculate rate of environmental changes"""
# This would compare with previous perception to detect changes
return 0.1 # Placeholder
def identify_attention_targets(self, objects: List) -> List[Dict]:
"""Identify objects that require attention"""
attention_targets = []
for obj in objects:
if obj.get('movement_velocity', 0) > 0.1: # Moving objects
attention_targets.append({
'object': obj,
'priority': 0.8,
'reason': 'moving_object'
})
elif obj.get('distance', float('inf')) < 1.0: # Close objects
attention_targets.append({
'object': obj,
'priority': 0.6,
'reason': 'close_object'
})
return attention_targets
class VisualPerception:
"""Visual perception component"""
def __init__(self):
self.feature_detector = self.initialize_feature_detector()
self.depth_processor = self.initialize_depth_processor()
self.semantic_segmenter = self.initialize_semantic_segmenter()
def initialize_feature_detector(self):
"""Initialize visual feature detection"""
# This would load a pre-trained model
return None
def initialize_depth_processor(self):
"""Initialize depth processing"""
# This would handle stereo or structured light depth
return None
def initialize_semantic_segmenter(self):
"""Initialize semantic segmentation"""
# This would load a segmentation model
return None
def process(self, camera_data: Dict, depth_data: Dict) -> Dict[str, Any]:
"""Process visual data"""
features = self.extract_features(camera_data)
depth_map = self.process_depth(depth_data)
semantic_map = self.perform_segmentation(camera_data)
return {
'features': features,
'depth_map': depth_map,
'semantic_map': semantic_map,
'optical_flow': self.calculate_optical_flow(camera_data),
'visual_attention': self.calculate_visual_attention(features)
}
def extract_features(self, image_data: Dict) -> List[Dict]:
"""Extract visual features"""
# This would implement feature extraction
return []
def process_depth(self, depth_data: Dict) -> np.ndarray:
"""Process depth information"""
return np.zeros((480, 640)) # Placeholder
def perform_segmentation(self, image_data: Dict) -> np.ndarray:
"""Perform semantic segmentation"""
return np.zeros((480, 640)) # Placeholder
def calculate_optical_flow(self, image_data: Dict) -> np.ndarray:
"""Calculate optical flow"""
return np.zeros((480, 640, 2)) # Placeholder
def calculate_visual_attention(self, features: List[Dict]) -> float:
"""Calculate visual attention based on feature density"""
return min(1.0, len(features) / 100.0) # Normalize feature count
class AuditoryPerception:
"""Auditory perception component"""
def __init__(self):
self.speech_recognizer = self.initialize_speech_recognizer()
self.sound_classifier = self.initialize_sound_classifier()
self.direction_estimator = self.initialize_direction_estimator()
def initialize_speech_recognizer(self):
"""Initialize speech recognition"""
return None
def initialize_sound_classifier(self):
"""Initialize sound classification"""
return None
def initialize_direction_estimator(self):
"""Initialize sound direction estimation"""
return None
def process(self, audio_data: Dict) -> Dict[str, Any]:
"""Process auditory data"""
speech_content = self.recognize_speech(audio_data)
sound_events = self.classify_sounds(audio_data)
sound_direction = self.estimate_direction(audio_data)
return {
'speech': speech_content,
'sounds': sound_events,
'direction': sound_direction,
'attention_cue': self.calculate_attention_cue(sound_events)
}
def recognize_speech(self, audio_data: Dict) -> Dict:
"""Recognize speech content"""
return {'text': '', 'confidence': 0.0} # Placeholder
def classify_sounds(self, audio_data: Dict) -> List[Dict]:
"""Classify sound events"""
return [] # Placeholder
def estimate_direction(self, audio_data: Dict) -> np.ndarray:
"""Estimate sound direction"""
return np.array([0.0, 0.0, 1.0]) # Placeholder
def calculate_attention_cue(self, sounds: List[Dict]) -> float:
"""Calculate auditory attention based on sound events"""
return 0.0 # Placeholder
class TactilePerception:
"""Tactile perception component"""
def __init__(self):
self.contact_detector = self.initialize_contact_detector()
self.force_analyzer = self.initialize_force_analyzer()
self.texture_analyzer = self.initialize_texture_analyzer()
def initialize_contact_detector(self):
"""Initialize contact detection"""
return None
def initialize_force_analyzer(self):
"""Initialize force analysis"""
return None
def initialize_texture_analyzer(self):
"""Initialize texture analysis"""
return None
def process(self, tactile_data: Dict) -> Dict[str, Any]:
"""Process tactile data"""
contact_info = self.detect_contact(tactile_data)
force_info = self.analyze_forces(tactile_data)
texture_info = self.analyze_texture(tactile_data)
return {
'contact': contact_info,
'forces': force_info,
'texture': texture_info,
'grasp_quality': self.calculate_grasp_quality(force_info)
}
def detect_contact(self, tactile_data: Dict) -> Dict:
"""Detect contact points"""
return {'contacts': [], 'contact_force': 0.0} # Placeholder
def analyze_forces(self, tactile_data: Dict) -> Dict:
"""Analyze force information"""
return {'normal_forces': [], 'shear_forces': []} # Placeholder
def analyze_texture(self, tactile_data: Dict) -> Dict:
"""Analyze texture information"""
return {'roughness': 0.0, 'pattern': ''} # Placeholder
def calculate_grasp_quality(self, force_info: Dict) -> float:
"""Calculate grasp quality based on forces"""
return 0.8 # Placeholder
class ProprioceptivePerception:
"""Proprioceptive perception component"""
def __init__(self):
self.joint_analyzer = self.initialize_joint_analyzer()
self.balance_analyzer = self.initialize_balance_analyzer()
self.motion_analyzer = self.initialize_motion_analyzer()
def initialize_joint_analyzer(self):
"""Initialize joint state analysis"""
return None
def initialize_balance_analyzer(self):
"""Initialize balance analysis"""
return None
def initialize_motion_analyzer(self):
"""Initialize motion analysis"""
return None
def process(self, joint_data: Dict, imu_data: Dict) -> Dict[str, Any]:
"""Process proprioceptive data"""
joint_state = self.analyze_joints(joint_data)
balance_state = self.analyze_balance(imu_data)
motion_state = self.analyze_motion(imu_data)
return {
'joint_state': joint_state,
'balance': balance_state,
'motion': motion_state,
'stability': self.calculate_stability(balance_state, motion_state)
}
def analyze_joints(self, joint_data: Dict) -> Dict:
"""Analyze joint positions and velocities"""
return {'positions': [], 'velocities': [], 'efforts': []} # Placeholder
def analyze_balance(self, imu_data: Dict) -> Dict:
"""Analyze balance based on IMU data"""
return {'com_position': [0, 0, 0], 'stability_margin': 0.1} # Placeholder
def analyze_motion(self, imu_data: Dict) -> Dict:
"""Analyze motion based on IMU data"""
return {'linear_acc': [0, 0, 0], 'angular_vel': [0, 0, 0]} # Placeholder
def calculate_stability(self, balance: Dict, motion: Dict) -> float:
"""Calculate stability metric"""
return 0.9 # Placeholder
class SensorFusion:
"""Fuses data from multiple sensors"""
def __init__(self):
self.fusion_algorithms = {
'kalman': self.kalman_fusion,
'particle': self.particle_fusion,
'neural': self.neural_fusion
}
def fuse(self, sensor_data: Dict[str, Any]) -> Dict[str, Any]:
"""Fuse data from multiple sensors"""
# Use neural fusion for complex multi-modal fusion
return self.neural_fusion(sensor_data)
def kalman_fusion(self, sensor_data: Dict[str, Any]) -> Dict[str, Any]:
"""Kalman filter-based fusion"""
# Implementation would use Kalman filters
return sensor_data # Placeholder
def particle_fusion(self, sensor_data: Dict[str, Any]) -> Dict[str, Any]:
"""Particle filter-based fusion"""
# Implementation would use particle filters
return sensor_data # Placeholder
def neural_fusion(self, sensor_data: Dict[str, Any]) -> Dict[str, Any]:
"""Neural network-based fusion"""
# This would implement a neural fusion network
# For now, return a simple weighted combination
fused_result = {}
# Combine visual and depth for 3D understanding
if 'visual' in sensor_data and 'depth' in sensor_data:
fused_result['3d_scene'] = self.combine_visual_depth(
sensor_data['visual'], sensor_data['depth']
)
# Combine proprioceptive with other modalities for self-awareness
if 'proprioceptive' in sensor_data:
fused_result['self_model'] = sensor_data['proprioceptive']
return fused_result
def combine_visual_depth(self, visual: Dict, depth: Dict) -> Dict:
"""Combine visual and depth information"""
return {
'point_cloud': 'generated_from_visual_depth',
'object_poses': 'estimated_from_fusion'
}
Planning and Reasoning Integration
Hierarchical Planning System
The planning module creates action sequences to achieve goals while considering environmental constraints, robot capabilities, and safety requirements.
class PlanningModule:
"""Hierarchical planning system for the AI-Robot Brain"""
def __init__(self):
self.task_planner = TaskPlanner()
self.motion_planner = MotionPlanner()
self.navigation_planner = NavigationPlanner()
self.temporal_planner = TemporalPlanner()
self.reasoning_engine = ReasoningEngine()
def process(self, planning_request: Dict[str, Any]) -> Dict[str, Any]:
"""Process planning request and generate action sequence"""
# Parse planning request
goal = planning_request.get('goal')
constraints = planning_request.get('constraints', {})
current_state = planning_request.get('current_state', {})
# Perform hierarchical planning
high_level_plan = self.task_planner.plan(goal, current_state, constraints)
# For each high-level task, generate motion plans
detailed_plans = []
for task in high_level_plan:
motion_plan = self.motion_planner.plan(
task['motion_primitive'],
current_state,
task.get('spatial_constraints', {})
)
detailed_plans.append(motion_plan)
# Generate navigation plan for spatial tasks
navigation_plan = self.navigation_planner.plan(
goal.get('location', None),
current_state,
constraints
)
# Integrate temporal aspects
temporal_plan = self.temporal_planner.integrate(
detailed_plans, navigation_plan, constraints
)
# Apply reasoning for validation and optimization
validated_plan = self.reasoning_engine.validate(temporal_plan)
return {
'high_level_plan': high_level_plan,
'detailed_plans': detailed_plans,
'navigation_plan': navigation_plan,
'temporal_plan': temporal_plan,
'validated_plan': validated_plan,
'plan_validity': self.assess_plan_validity(validated_plan),
'goal_urgency': self.calculate_goal_urgency(goal),
'execution_risk': self.assess_execution_risk(validated_plan)
}
def assess_plan_validity(self, plan: Dict) -> float:
"""Assess the validity of a plan"""
# Check for consistency, feasibility, and safety
validity_score = 1.0
# Check for kinematic feasibility
if not self.check_kinematic_feasibility(plan):
validity_score *= 0.5
# Check for collision avoidance
if not self.check_collision_avoidance(plan):
validity_score *= 0.3
# Check for dynamic constraints
if not self.check_dynamic_constraints(plan):
validity_score *= 0.7
return max(0.0, min(1.0, validity_score))
def calculate_goal_urgency(self, goal: Dict) -> float:
"""Calculate urgency of goal achievement"""
# Higher urgency for time-sensitive or safety-critical goals
if goal.get('type') == 'safety':
return 1.0
elif goal.get('deadline'):
time_left = goal['deadline'] - time.time()
if time_left < 30: # 30 seconds
return 0.9
elif time_left < 300: # 5 minutes
return 0.7
return 0.3 # Normal urgency
def assess_execution_risk(self, plan: Dict) -> float:
"""Assess risk of plan execution"""
# Calculate risk based on environmental factors and plan complexity
risk_score = 0.1 # Base risk
# Add risk for complex maneuvers
if plan.get('complexity', 0) > 5:
risk_score += 0.2
# Add risk for uncertain environments
if plan.get('environment_uncertainty', 0) > 0.5:
risk_score += 0.3
# Add risk for safety-critical operations
if plan.get('safety_critical', False):
risk_score += 0.4
return min(1.0, risk_score)
def check_kinematic_feasibility(self, plan: Dict) -> bool:
"""Check if plan is kinematically feasible"""
return True # Placeholder
def check_collision_avoidance(self, plan: Dict) -> bool:
"""Check if plan avoids collisions"""
return True # Placeholder
def check_dynamic_constraints(self, plan: Dict) -> bool:
"""Check if plan respects dynamic constraints"""
return True # Placeholder
class TaskPlanner:
"""High-level task planning"""
def __init__(self):
self.task_library = self.initialize_task_library()
self.hierarchical_decomposer = HierarchicalDecomposer()
def initialize_task_library(self) -> Dict:
"""Initialize library of known tasks"""
return {
'navigate': {
'primitives': ['walk', 'turn', 'step'],
'constraints': ['collision_free', 'stable']
},
'manipulate': {
'primitives': ['reach', 'grasp', 'move'],
'constraints': ['reachable', 'graspable']
},
'communicate': {
'primitives': ['speak', 'gesture', 'listen'],
'constraints': ['attention', 'context']
}
}
def plan(self, goal: Dict, current_state: Dict, constraints: Dict) -> List[Dict]:
"""Plan high-level tasks to achieve goal"""
# Decompose goal into subtasks
subtasks = self.hierarchical_decomposer.decompose(goal)
# Validate and sequence subtasks
validated_tasks = []
for task in subtasks:
if self.validate_task(task, current_state, constraints):
validated_tasks.append(task)
return validated_tasks
def validate_task(self, task: Dict, current_state: Dict, constraints: Dict) -> bool:
"""Validate if task is achievable given current state and constraints"""
task_type = task.get('type')
if task_type not in self.task_library:
return False
task_constraints = self.task_library[task_type]['constraints']
# Check each constraint
for constraint in task_constraints:
if not self.check_constraint(task, current_state, constraint):
return False
return True
def check_constraint(self, task: Dict, current_state: Dict, constraint: str) -> bool:
"""Check if a specific constraint is satisfied"""
# Implementation would check specific constraints
return True # Placeholder
class MotionPlanner:
"""Low-level motion planning"""
def __init__(self):
self.ik_solver = self.initialize_ik_solver()
self.trajectory_generator = TrajectoryGenerator()
self.collision_checker = CollisionChecker()
def initialize_ik_solver(self):
"""Initialize inverse kinematics solver"""
return None
def plan(self, motion_primitive: str, current_state: Dict, constraints: Dict) -> Dict:
"""Plan motion for a specific primitive"""
if motion_primitive == 'reach':
return self.plan_reach(current_state, constraints)
elif motion_primitive == 'grasp':
return self.plan_grasp(current_state, constraints)
elif motion_primitive == 'walk':
return self.plan_walk(current_state, constraints)
else:
return self.plan_generic(motion_primitive, current_state, constraints)
def plan_reach(self, current_state: Dict, constraints: Dict) -> Dict:
"""Plan reaching motion"""
target = constraints.get('target_position')
if not target:
return {'success': False, 'error': 'No target specified'}
# Solve inverse kinematics
joint_trajectory = self.solve_ik_for_reach(target, current_state)
# Generate smooth trajectory
trajectory = self.trajectory_generator.generate_smooth_trajectory(
joint_trajectory, constraints
)
return {
'success': True,
'trajectory': trajectory,
'ik_solution': joint_trajectory,
'collision_free': self.collision_checker.check_trajectory(trajectory)
}
def plan_grasp(self, current_state: Dict, constraints: Dict) -> Dict:
"""Plan grasping motion"""
object_info = constraints.get('object_info')
if not object_info:
return {'success': False, 'error': 'No object specified'}
# Determine grasp pose
grasp_pose = self.calculate_grasp_pose(object_info)
# Plan approach and grasp
approach_trajectory = self.plan_approach(grasp_pose, current_state)
grasp_trajectory = self.plan_grasp_execution(grasp_pose, current_state)
return {
'success': True,
'approach_trajectory': approach_trajectory,
'grasp_trajectory': grasp_trajectory,
'grasp_pose': grasp_pose
}
def plan_walk(self, current_state: Dict, constraints: Dict) -> Dict:
"""Plan walking motion"""
target_location = constraints.get('target_location')
if not target_location:
return {'success': False, 'error': 'No target location'}
# Generate walking pattern
walking_pattern = self.generate_walking_pattern(
current_state, target_location, constraints
)
return {
'success': True,
'walking_pattern': walking_pattern,
'step_sequence': self.extract_step_sequence(walking_pattern)
}
def plan_generic(self, motion_primitive: str, current_state: Dict, constraints: Dict) -> Dict:
"""Plan generic motion primitive"""
# This would handle other motion types
return {'success': True, 'primitive': motion_primitive}
def solve_ik_for_reach(self, target: np.ndarray, current_state: Dict) -> List[np.ndarray]:
"""Solve inverse kinematics for reaching"""
# This would implement IK solver
return [np.zeros(6)] # Placeholder
def calculate_grasp_pose(self, object_info: Dict) -> Dict:
"""Calculate optimal grasp pose for object"""
# This would analyze object shape and determine grasp pose
return {'position': [0, 0, 0], 'orientation': [1, 0, 0, 0]} # Placeholder
def plan_approach(self, grasp_pose: Dict, current_state: Dict) -> List[Dict]:
"""Plan approach trajectory to grasp pose"""
return [] # Placeholder
def plan_grasp_execution(self, grasp_pose: Dict, current_state: Dict) -> List[Dict]:
"""Plan grasp execution trajectory"""
return [] # Placeholder
def generate_walking_pattern(self, current_state: Dict, target: np.ndarray, constraints: Dict) -> Dict:
"""Generate walking pattern to reach target"""
return {'steps': [], 'timing': []} # Placeholder
def extract_step_sequence(self, walking_pattern: Dict) -> List[Dict]:
"""Extract step sequence from walking pattern"""
return [] # Placeholder
class NavigationPlanner:
"""Navigation planning for spatial tasks"""
def __init__(self):
self.path_planner = PathPlanner()
self.map_manager = MapManager()
self.obstacle_avoider = ObstacleAvoider()
def plan(self, target_location: Optional[np.ndarray], current_state: Dict, constraints: Dict) -> Dict:
"""Plan navigation to target location"""
if target_location is None:
return {'success': True, 'path': [], 'type': 'stationary'}
# Get current map
current_map = self.map_manager.get_current_map()
# Plan path to target
path = self.path_planner.plan_path(
current_state['position'],
target_location,
current_map,
constraints
)
# Plan obstacle avoidance if needed
if constraints.get('dynamic_obstacles', False):
path = self.obstacle_avoider.plan_avoidance(path, current_state)
return {
'success': len(path) > 0,
'path': path,
'waypoints': self.extract_waypoints(path),
'navigation_strategy': self.select_navigation_strategy(path, constraints)
}
def extract_waypoints(self, path: List[np.ndarray]) -> List[Dict]:
"""Extract waypoints from path"""
waypoints = []
for i, point in enumerate(path):
waypoints.append({
'id': i,
'position': point.tolist(),
'type': 'waypoint'
})
return waypoints
def select_navigation_strategy(self, path: List[np.ndarray], constraints: Dict) -> str:
"""Select appropriate navigation strategy"""
path_length = sum(np.linalg.norm(path[i] - path[i-1]) for i in range(1, len(path)))
if path_length < 1.0: # Short distance
return 'precise_navigation'
elif constraints.get('crowded_environment', False):
return 'social_navigation'
else:
return 'efficient_navigation'
class TemporalPlanner:
"""Temporal planning and scheduling"""
def __init__(self):
self.scheduler = TaskScheduler()
self.temporal_reasoner = TemporalReasoner()
def integrate(self, detailed_plans: List[Dict], navigation_plan: Dict, constraints: Dict) -> Dict:
"""Integrate plans temporally"""
# Create temporal schedule
temporal_schedule = self.scheduler.create_schedule(
detailed_plans, navigation_plan, constraints
)
# Apply temporal reasoning
validated_schedule = self.temporal_reasoner.validate(temporal_schedule)
return validated_schedule
class ReasoningEngine:
"""Logical reasoning component"""
def __init__(self):
self.knowledge_base = KnowledgeBase()
self.inference_engine = InferenceEngine()
self.planning_reasoner = PlanningReasoner()
def validate(self, plan: Dict) -> Dict:
"""Validate plan using logical reasoning"""
# Check plan consistency
if not self.planning_reasoner.check_consistency(plan):
plan['valid'] = False
plan['reason'] = 'inconsistent_plan'
# Check logical preconditions
if not self.planning_reasoner.check_preconditions(plan):
plan['valid'] = False
plan['reason'] = 'unmet_preconditions'
# Check for conflicts
conflicts = self.planning_reasoner.detect_conflicts(plan)
if conflicts:
plan['conflicts'] = conflicts
return plan
class KnowledgeBase:
"""Knowledge representation and storage"""
def __init__(self):
self.facts = {}
self.rules = []
self.ontologies = {}
def add_fact(self, fact: Dict):
"""Add a fact to the knowledge base"""
key = fact.get('id', len(self.facts))
self.facts[key] = fact
def add_rule(self, rule: Dict):
"""Add a rule to the knowledge base"""
self.rules.append(rule)
class InferenceEngine:
"""Logical inference engine"""
def __init__(self):
self.reasoning_strategies = {
'forward_chaining': self.forward_chaining,
'backward_chaining': self.backward_chaining,
'resolution': self.resolution
}
def forward_chaining(self, facts: List[Dict], rules: List[Dict]) -> List[Dict]:
"""Forward chaining inference"""
# Implementation would perform forward chaining
return facts # Placeholder
def backward_chaining(self, goal: Dict, facts: List[Dict], rules: List[Dict]) -> bool:
"""Backward chaining inference"""
# Implementation would perform backward chaining
return True # Placeholder
def resolution(self, clauses: List[Dict]) -> List[Dict]:
"""Resolution-based inference"""
# Implementation would perform resolution
return clauses # Placeholder
class PlanningReasoner:
"""Specialized reasoning for planning validation"""
def check_consistency(self, plan: Dict) -> bool:
"""Check if plan is logically consistent"""
return True # Placeholder
def check_preconditions(self, plan: Dict) -> bool:
"""Check if plan preconditions are met"""
return True # Placeholder
def detect_conflicts(self, plan: Dict) -> List[Dict]:
"""Detect conflicts in plan"""
return [] # Placeholder
Control System Integration
Real-Time Control Architecture
The control module executes planned actions while maintaining stability and safety. It operates at high frequencies to ensure responsive behavior.
class ControlModule:
"""Real-time control system for the AI-Robot Brain"""
def __init__(self):
self.low_level_controllers = LowLevelControllers()
self.high_level_controller = HighLevelController()
self.safety_controller = SafetyController()
self.adaptive_controller = AdaptiveController()
self.feedback_processor = FeedbackProcessor()
def process(self, control_request: Dict[str, Any]) -> Dict[str, Any]:
"""Process control request and generate motor commands"""
# Parse control request
plan = control_request.get('plan', {})
current_state = control_request.get('current_state', {})
feedback = control_request.get('feedback', {})
# Update feedback processing
processed_feedback = self.feedback_processor.process(feedback)
# Generate low-level commands
low_level_commands = self.low_level_controllers.generate_commands(
plan, current_state, processed_feedback
)
# Apply high-level control logic
high_level_commands = self.high_level_controller.process(
low_level_commands, plan, current_state
)
# Apply safety constraints
safe_commands = self.safety_controller.apply_safety(
high_level_commands, current_state, processed_feedback
)
# Apply adaptive control adjustments
adaptive_commands = self.adaptive_controller.adjust(
safe_commands, processed_feedback
)
# Update internal state
self.update_control_state(adaptive_commands, current_state)
return {
'motor_commands': adaptive_commands,
'control_state': self.get_control_state(),
'stability': self.calculate_stability(current_state),
'safety_critical': self.is_safety_critical(current_state),
'control_effort': self.calculate_control_effort(adaptive_commands)
}
def update_control_state(self, commands: Dict, state: Dict):
"""Update internal control state"""
pass
def get_control_state(self) -> Dict:
"""Get current control state"""
return {
'stability_margin': 0.1,
'control_effort': 0.5,
'safety_status': 'normal'
}
def calculate_stability(self, state: Dict) -> float:
"""Calculate stability metric"""
# This would analyze center of mass, support polygon, etc.
return 0.9 # Placeholder
def is_safety_critical(self, state: Dict) -> bool:
"""Check if current situation is safety critical"""
# This would check for dangerous states
return False # Placeholder
def calculate_control_effort(self, commands: Dict) -> float:
"""Calculate control effort metric"""
# This would measure the magnitude of control signals
return 0.3 # Placeholder
class LowLevelControllers:
"""Low-level motor controllers"""
def __init__(self):
self.joint_controllers = {}
self.impedance_controllers = {}
self.force_controllers = {}
def generate_commands(self, plan: Dict, current_state: Dict, feedback: Dict) -> Dict:
"""Generate low-level motor commands"""
commands = {}
# Process different types of control commands
if 'joint_trajectory' in plan:
commands['joint'] = self.execute_joint_trajectory(
plan['joint_trajectory'], current_state
)
if 'impedance_control' in plan:
commands['impedance'] = self.execute_impedance_control(
plan['impedance_control'], current_state
)
if 'force_control' in plan:
commands['force'] = self.execute_force_control(
plan['force_control'], current_state
)
if 'walking_pattern' in plan:
commands['walking'] = self.execute_walking_pattern(
plan['walking_pattern'], current_state
)
return commands
def execute_joint_trajectory(self, trajectory: List[Dict], current_state: Dict) -> Dict:
"""Execute joint space trajectory"""
current_positions = current_state.get('joint_positions', [])
target_positions = trajectory[0].get('positions', current_positions)
# Calculate position, velocity, acceleration commands
position_commands = self.interpolate_trajectory(
current_positions, target_positions, 0.01 # 10ms control cycle
)
return {
'positions': position_commands,
'velocities': self.calculate_velocities(position_commands),
'accelerations': self.calculate_accelerations(position_commands),
'gains': self.calculate_appropriate_gains(position_commands)
}
def execute_impedance_control(self, impedance_spec: Dict, current_state: Dict) -> Dict:
"""Execute impedance control"""
stiffness = impedance_spec.get('stiffness', 1000.0)
damping = impedance_spec.get('damping', 20.0)
desired_pose = impedance_spec.get('desired_pose', [0, 0, 0, 1, 0, 0, 0])
return {
'stiffness': stiffness,
'damping': damping,
'desired_pose': desired_pose,
'control_mode': 'impedance'
}
def execute_force_control(self, force_spec: Dict, current_state: Dict) -> Dict:
"""Execute force control"""
desired_force = force_spec.get('force', [0, 0, 0])
force_frame = force_spec.get('frame', 'world')
return {
'desired_force': desired_force,
'force_frame': force_frame,
'control_mode': 'force'
}
def execute_walking_pattern(self, walking_pattern: Dict, current_state: Dict) -> Dict:
"""Execute walking pattern"""
step_sequence = walking_pattern.get('steps', [])
timing = walking_pattern.get('timing', [])
return {
'step_sequence': step_sequence,
'timing': timing,
'control_mode': 'walking',
'gait_parameters': self.calculate_gait_parameters(step_sequence, timing)
}
def interpolate_trajectory(self, start: List[float], end: List[float], dt: float) -> List[float]:
"""Interpolate between start and end positions"""
# Simple linear interpolation - in practice, this would use splines
alpha = dt # For 10ms, use full interpolation
interpolated = [
start[i] * (1 - alpha) + end[i] * alpha
for i in range(len(start))
]
return interpolated
def calculate_velocities(self, positions: List[float]) -> List[float]:
"""Calculate desired velocities"""
return [0.0] * len(positions) # Placeholder
def calculate_accelerations(self, positions: List[float]) -> List[float]:
"""Calculate desired accelerations"""
return [0.0] * len(positions) # Placeholder
def calculate_appropriate_gains(self, commands: List[float]) -> Dict:
"""Calculate appropriate control gains"""
return {
'position_gains': [100.0] * len(commands), # High gain for precision
'velocity_gains': [10.0] * len(commands), # Damping
'feedforward_gains': [1.0] * len(commands) # Feedforward
}
def calculate_gait_parameters(self, steps: List[Dict], timing: List[float]) -> Dict:
"""Calculate gait parameters for walking"""
return {
'step_height': 0.05, # 5cm step height
'step_length': 0.3, # 30cm step length
'swing_time': 0.8, # 80% of step time in swing
'double_support_time': 0.1 # 10% double support
}
class HighLevelController:
"""High-level control logic"""
def __init__(self):
self.balance_controller = BalanceController()
self.task_controller = TaskController()
self.mode_manager = ModeManager()
def process(self, low_level_commands: Dict, plan: Dict, current_state: Dict) -> Dict:
"""Process high-level control logic"""
# Apply balance control if needed
if self.is_balancing_required(current_state):
balance_adjustment = self.balance_controller.adjust(
low_level_commands, current_state
)
low_level_commands = self.merge_commands(
low_level_commands, balance_adjustment
)
# Apply task-specific control
task_adjustment = self.task_controller.adjust(
low_level_commands, plan, current_state
)
high_level_commands = self.merge_commands(
low_level_commands, task_adjustment
)
# Manage control modes
mode_commands = self.mode_manager.manage_modes(
high_level_commands, plan, current_state
)
return mode_commands
def is_balancing_required(self, current_state: Dict) -> bool:
"""Check if balance control is required"""
stability_margin = current_state.get('stability_margin', 0.1)
return stability_margin < 0.05 # Require balance if margin is small
def merge_commands(self, base_commands: Dict, adjustment: Dict) -> Dict:
"""Merge base commands with adjustments"""
merged = base_commands.copy()
# Add adjustments to base commands
for key, value in adjustment.items():
if key in merged:
if isinstance(value, dict) and isinstance(merged[key], dict):
merged[key].update(value)
elif isinstance(value, list) and isinstance(merged[key], list):
# Simple element-wise addition
merged[key] = [
b + a for b, a in zip(merged[key], value)
][:len(merged[key])]
else:
merged[key] = value
else:
merged[key] = value
return merged
class BalanceController:
"""Balance control for humanoid robots"""
def __init__(self):
self.com_controller = CenterOfMassController()
self.zmp_controller = ZeroMomentPointController()
self.ankle_strategy = AnkleStrategy()
self.hip_strategy = HipStrategy()
def adjust(self, commands: Dict, current_state: Dict) -> Dict:
"""Adjust commands for balance"""
# Calculate current balance state
balance_state = self.calculate_balance_state(current_state)
# Select appropriate balance strategy
if balance_state['disturbance_magnitude'] < 0.05:
strategy = self.ankle_strategy
elif balance_state['disturbance_magnitude'] < 0.1:
strategy = self.hip_strategy
else:
strategy = self.com_controller # Large disturbances
# Apply balance strategy
balance_adjustment = strategy.apply(
balance_state, current_state
)
return balance_adjustment
def calculate_balance_state(self, current_state: Dict) -> Dict:
"""Calculate current balance state"""
com_position = current_state.get('com_position', [0, 0, 0])
com_velocity = current_state.get('com_velocity', [0, 0, 0])
support_polygon = current_state.get('support_polygon', [])
# Calculate balance metrics
com_projection = [com_position[0], com_position[1]] # Project to ground
distance_to_support = self.distance_to_polygon(com_projection, support_polygon)
return {
'com_position': com_position,
'com_velocity': com_velocity,
'support_polygon': support_polygon,
'com_projection': com_projection,
'distance_to_support': distance_to_support,
'disturbance_magnitude': abs(distance_to_support),
'balance_margin': distance_to_support
}
def distance_to_polygon(self, point: List[float], polygon: List[List[float]]) -> float:
"""Calculate distance from point to polygon"""
# This would implement point-to-polygon distance calculation
return 0.1 # Placeholder
class CenterOfMassController:
"""Center of mass controller"""
def apply(self, balance_state: Dict, current_state: Dict) -> Dict:
"""Apply center of mass control"""
# Calculate desired COM trajectory to return to stable position
desired_com_trajectory = self.calculate_com_trajectory(balance_state)
return {
'com_control': {
'desired_trajectory': desired_com_trajectory,
'control_gains': self.get_control_gains()
}
}
def calculate_com_trajectory(self, balance_state: Dict) -> List[Dict]:
"""Calculate COM trajectory to restore balance"""
return [{'time': 0.0, 'position': balance_state['com_position']}]
def get_control_gains(self) -> Dict:
"""Get control gains for COM control"""
return {'kp': 100.0, 'kd': 20.0, 'ki': 10.0}
class ZeroMomentPointController:
"""Zero moment point controller"""
def apply(self, balance_state: Dict, current_state: Dict) -> Dict:
"""Apply ZMP control"""
return {
'zmp_control': {
'desired_zmp': [0.0, 0.0],
'current_zmp': [0.0, 0.0],
'control_gains': {'kp': 50.0, 'kd': 10.0}
}
}
class AnkleStrategy:
"""Ankle strategy for small balance disturbances"""
def apply(self, balance_state: Dict, current_state: Dict) -> Dict:
"""Apply ankle strategy control"""
return {
'ankle_control': {
'torques': [0.0, 0.0, 0.0], # Roll, pitch, yaw torques
'angles': [0.0, 0.0, 0.0], # Desired angles
'gains': {'kp': 200.0, 'kd': 40.0}
}
}
class HipStrategy:
"""Hip strategy for medium balance disturbances"""
def apply(self, balance_state: Dict, current_state: Dict) -> Dict:
"""Apply hip strategy control"""
return {
'hip_control': {
'torques': [0.0, 0.0, 0.0],
'angles': [0.0, 0.0, 0.0],
'gains': {'kp': 150.0, 'kd': 30.0}
}
}
class TaskController:
"""Task-specific control adjustments"""
def __init__(self):
self.task_specific_controllers = {
'walking': WalkingTaskController(),
'manipulation': ManipulationTaskController(),
'standing': StandingTaskController(),
'sitting': SittingTaskController()
}
def adjust(self, commands: Dict, plan: Dict, current_state: Dict) -> Dict:
"""Apply task-specific control adjustments"""
task_type = plan.get('task_type', 'default')
if task_type in self.task_specific_controllers:
task_controller = self.task_specific_controllers[task_type]
task_adjustment = task_controller.adjust(commands, plan, current_state)
else:
task_adjustment = {}
return task_adjustment
class WalkingTaskController:
"""Control adjustments for walking tasks"""
def adjust(self, commands: Dict, plan: Dict, current_state: Dict) -> Dict:
"""Adjust controls for walking"""
walking_pattern = plan.get('walking_pattern', {})
return {
'walking_control': {
'step_timing': walking_pattern.get('timing', []),
'foot_placement': self.calculate_foot_placement(current_state),
'arm_swing': self.calculate_arm_swing(),
'trunk_stabilization': True
}
}
def calculate_foot_placement(self, current_state: Dict) -> List[Dict]:
"""Calculate desired foot placement"""
return [{'left_foot': [0.1, 0, 0], 'right_foot': [0.1, 0, 0]}]
def calculate_arm_swing(self) -> Dict:
"""Calculate arm swing for walking"""
return {'left_arm': 0.2, 'right_arm': -0.2} # Oscillating arms
class ManipulationTaskController:
"""Control adjustments for manipulation tasks"""
def adjust(self, commands: Dict, plan: Dict, current_state: Dict) -> Dict:
"""Adjust controls for manipulation"""
return {
'manipulation_control': {
'arm_impedance': 500.0, # Lower impedance for compliance
'gripper_force': 20.0, # Controlled gripping force
'trunk_stabilization': True,
'base_stabilization': True
}
}
class SafetyController:
"""Safety control system"""
def __init__(self):
self.safety_limits = self.define_safety_limits()
self.emergency_stop = EmergencyStopSystem()
def define_safety_limits(self) -> Dict:
"""Define safety limits for all control aspects"""
return {
'joint_limits': {
'position': {'min': -3.14, 'max': 3.14}, # ±180°
'velocity': {'max': 5.0}, # 5 rad/s
'effort': {'max': 100.0} # 100 Nm
},
'balance_limits': {
'com_margin': 0.02, # 2cm safety margin
'zmp_margin': 0.05 # 5cm safety margin
},
'collision_limits': {
'minimum_distance': 0.1, # 10cm minimum
'maximum_approach_speed': 0.5 # 0.5 m/s maximum
}
}
def apply_safety(self, commands: Dict, current_state: Dict, feedback: Dict) -> Dict:
"""Apply safety constraints to commands"""
# Check joint limits
safe_commands = self.enforce_joint_limits(commands, current_state)
# Check balance safety
if not self.is_balanced(current_state):
safe_commands = self.apply_balance_recovery(safe_commands, current_state)
# Check collision safety
if self.detect_collision_risk(current_state, commands):
safe_commands = self.apply_collision_avoidance(safe_commands, current_state)
# Check emergency conditions
if self.emergency_stop.should_stop(current_state, feedback):
safe_commands = self.emergency_stop.get_stop_commands()
return safe_commands
def enforce_joint_limits(self, commands: Dict, current_state: Dict) -> Dict:
"""Enforce joint position, velocity, and effort limits"""
safe_commands = commands.copy()
if 'joint' in safe_commands:
joint_cmd = safe_commands['joint']
# Limit positions
joint_cmd['positions'] = [
max(self.safety_limits['joint_limits']['position']['min'],
min(pos, self.safety_limits['joint_limits']['position']['max']))
for pos in joint_cmd.get('positions', [])
]
# Limit velocities
joint_cmd['velocities'] = [
max(-self.safety_limits['joint_limits']['velocity']['max'],
min(vel, self.safety_limits['joint_limits']['velocity']['max']))
for vel in joint_cmd.get('velocities', [])
]
# Limit efforts
joint_cmd['efforts'] = [
max(-self.safety_limits['joint_limits']['effort']['max'],
min(eff, self.safety_limits['joint_limits']['effort']['max']))
for eff in joint_cmd.get('efforts', [])
]
return safe_commands
def is_balanced(self, current_state: Dict) -> bool:
"""Check if robot is in safe balance state"""
com_margin = current_state.get('com_margin', 0.1)
return com_margin > self.safety_limits['balance_limits']['com_margin']
def apply_balance_recovery(self, commands: Dict, current_state: Dict) -> Dict:
"""Apply balance recovery measures"""
# This would implement balance recovery strategies
return commands
def detect_collision_risk(self, current_state: Dict, commands: Dict) -> bool:
"""Detect potential collision risk"""
# This would check planned motion against obstacle map
return False
def apply_collision_avoidance(self, commands: Dict, current_state: Dict) -> Dict:
"""Apply collision avoidance measures"""
# This would modify commands to avoid collisions
return commands
class AdaptiveController:
"""Adaptive control for changing conditions"""
def __init__(self):
self.adaptation_engine = AdaptationEngine()
self.performance_monitor = PerformanceMonitor()
def adjust(self, commands: Dict, feedback: Dict) -> Dict:
"""Apply adaptive adjustments based on feedback"""
# Monitor performance
performance_metrics = self.performance_monitor.evaluate(feedback)
# Determine adaptation needs
adaptation_needed = self.adaptation_engine.is_adaptation_needed(
performance_metrics
)
if adaptation_needed:
adaptation_adjustment = self.adaptation_engine.calculate_adjustment(
commands, feedback, performance_metrics
)
# Merge adaptation with original commands
adapted_commands = self.merge_adaptation(
commands, adaptation_adjustment
)
else:
adapted_commands = commands
return adapted_commands
def merge_adaptation(self, original: Dict, adaptation: Dict) -> Dict:
"""Merge adaptation adjustment with original commands"""
# This would intelligently merge adaptation with original commands
return original
class FeedbackProcessor:
"""Process sensor feedback for control"""
def __init__(self):
self.feedback_filters = FeedbackFilters()
self.state_estimator = StateEstimator()
def process(self, raw_feedback: Dict) -> Dict:
"""Process raw feedback into usable control information"""
# Filter raw feedback
filtered_feedback = self.feedback_filters.apply(raw_feedback)
# Estimate current state
estimated_state = self.state_estimator.estimate(filtered_feedback)
return {
'filtered': filtered_feedback,
'estimated_state': estimated_state,
'reliability': self.assess_reliability(filtered_feedback)
}
def assess_reliability(self, feedback: Dict) -> float:
"""Assess reliability of feedback"""
return 0.95 # Placeholder
Learning System Integration
Continuous Learning and Adaptation
The learning module enables the AI-Robot Brain to continuously improve its performance through experience and adaptation to new situations.
import torch
import torch.nn as nn
import numpy as np
from typing import Dict, List, Any, Tuple
import pickle
import os
from collections import deque
import random
class LearningModule:
"""Learning and adaptation system for the AI-Robot Brain"""
def __init__(self):
self.supervised_learner = SupervisedLearner()
self.reinforcement_learner = ReinforcementLearner()
self.transfer_learner = TransferLearner()
self.meta_learner = MetaLearner()
self.memory_system = MemorySystem()
self.experience_replay = ExperienceReplay()
self.performance_analyzer = PerformanceAnalyzer()
def process(self, learning_request: Dict[str, Any]) -> Dict[str, Any]:
"""Process learning request and update cognitive models"""
# Parse learning request
task_type = learning_request.get('task_type', 'general')
experience_data = learning_request.get('experience_data', {})
performance_feedback = learning_request.get('performance_feedback', {})
learning_objective = learning_request.get('objective', 'improve_performance')
# Store experience
self.memory_system.store_experience(experience_data)
# Update learning models based on task type
if task_type == 'supervised':
learning_results = self.supervised_learner.update(
experience_data, learning_objective
)
elif task_type == 'reinforcement':
learning_results = self.reinforcement_learner.update(
experience_data, performance_feedback, learning_objective
)
elif task_type == 'transfer':
learning_results = self.transfer_learner.update(
experience_data, learning_objective
)
elif task_type == 'meta':
learning_results = self.meta_learner.update(
experience_data, learning_objective
)
else:
# General learning combining multiple approaches
learning_results = self.general_learning_update(
experience_data, performance_feedback, learning_objective
)
# Perform experience replay for consolidation
self.experience_replay.consolidate()
# Analyze performance improvements
performance_analysis = self.performance_analyzer.analyze(
learning_results, performance_feedback
)
return {
'learning_results': learning_results,
'performance_analysis': performance_analysis,
'novelty_score': self.calculate_novelty(experience_data),
'learning_opportunity': self.assess_learning_opportunity(experience_data),
'model_updates': learning_results.get('model_updates', 0),
'improvement_rate': performance_analysis.get('improvement_rate', 0.0)
}
def general_learning_update(self, experience: Dict, feedback: Dict, objective: str) -> Dict:
"""General learning update combining multiple approaches"""
results = {}
# Supervised learning from demonstrations
if 'demonstration' in experience:
sup_results = self.supervised_learner.update(
{'input': experience['demonstration']['input'],
'target': experience['demonstration']['output']},
objective
)
results.update(sup_results)
# Reinforcement learning from rewards
if 'reward' in feedback:
rl_results = self.reinforcement_learner.update(
experience, feedback, objective
)
results.update(rl_results)
# Transfer learning from similar tasks
transfer_results = self.transfer_learner.update(
experience, objective
)
results.update(transfer_results)
# Meta-learning for faster adaptation
meta_results = self.meta_learner.update(
experience, objective
)
results.update(meta_results)
return results
def calculate_novelty(self, experience: Dict) -> float:
"""Calculate novelty of experience"""
# This would compare with stored experiences to detect novelty
return 0.1 # Placeholder
def assess_learning_opportunity(self, experience: Dict) -> float:
"""Assess learning opportunity in experience"""
# Higher opportunity for experiences with clear outcomes
return 0.5 # Placeholder
class SupervisedLearner:
"""Supervised learning for imitation and classification tasks"""
def __init__(self):
self.models = {}
self.optimizers = {}
self.loss_functions = {}
self.training_data = {}
def update(self, experience: Dict, objective: str) -> Dict:
"""Update supervised learning models"""
# Identify the type of supervised learning needed
if 'demonstration' in experience:
return self.update_from_demonstration(experience['demonstration'], objective)
elif 'classification' in experience:
return self.update_classification_model(experience['classification'], objective)
else:
return {'success': False, 'error': 'Unknown supervised task'}
def update_from_demonstration(self, demo: Dict, objective: str) -> Dict:
"""Update model from demonstration data"""
# Extract input-output pairs from demonstration
inputs = demo.get('inputs', [])
outputs = demo.get('outputs', [])
task_type = demo.get('task_type', 'general')
# Create or get appropriate model
if task_type not in self.models:
self.models[task_type] = self.create_model_for_task(task_type)
self.optimizers[task_type] = torch.optim.Adam(
self.models[task_type].parameters()
)
self.loss_functions[task_type] = nn.MSELoss()
# Prepare data
input_tensor = torch.tensor(inputs, dtype=torch.float32)
target_tensor = torch.tensor(outputs, dtype=torch.float32)
# Train model
self.models[task_type].train()
optimizer = self.optimizers[task_type]
loss_fn = self.loss_functions[task_type]
optimizer.zero_grad()
predictions = self.models[task_type](input_tensor)
loss = loss_fn(predictions, target_tensor)
loss.backward()
optimizer.step()
return {
'success': True,
'task_type': task_type,
'loss': loss.item(),
'model_updates': 1,
'learning_rate': optimizer.param_groups[0]['lr']
}
def update_classification_model(self, classification_data: Dict, objective: str) -> Dict:
"""Update classification model"""
# Implementation for classification learning
return {'success': True, 'task_type': 'classification', 'model_updates': 1}
def create_model_for_task(self, task_type: str) -> nn.Module:
"""Create appropriate model for task type"""
if task_type == 'motion_planning':
return MotionPlanningNet()
elif task_type == 'object_recognition':
return ObjectRecognitionNet()
elif task_type == 'grasp_prediction':
return GraspPredictionNet()
else:
return GeneralNet()
class ReinforcementLearner:
"""Reinforcement learning for goal-directed behavior"""
def __init__(self):
self.agents = {}
self.environments = {}
self.replay_buffers = {}
self.training_stats = {}
def update(self, experience: Dict, feedback: Dict, objective: str) -> Dict:
"""Update reinforcement learning agents"""
# Extract reinforcement learning components
state = experience.get('state', {})
action = experience.get('action', {})
reward = feedback.get('reward', 0.0)
next_state = experience.get('next_state', {})
done = experience.get('done', False)
task_name = experience.get('task_name', 'default')
# Create or get RL agent
if task_name not in self.agents:
self.agents[task_name] = self.create_rl_agent(task_name)
self.replay_buffers[task_name] = deque(maxlen=10000)
# Store experience in replay buffer
self.replay_buffers[task_name].append({
'state': state,
'action': action,
'reward': reward,
'next_state': next_state,
'done': done
})
# Train agent if enough experience
if len(self.replay_buffers[task_name]) > 100:
training_result = self.train_agent(task_name)
else:
training_result = {'success': False, 'reason': 'insufficient_experience'}
return {
'success': True,
'task_name': task_name,
'training_result': training_result,
'buffer_size': len(self.replay_buffers[task_name]),
'total_reward': reward
}
def create_rl_agent(self, task_name: str) -> Any:
"""Create appropriate RL agent for task"""
if 'navigation' in task_name.lower():
return NavigationDQNAgent()
elif 'manipulation' in task_name.lower():
return ManipulationPPOAgent()
elif 'locomotion' in task_name.lower():
return LocomotionSACAgent()
else:
return GeneralRLAgent()
def train_agent(self, task_name: str) -> Dict:
"""Train RL agent with experience replay"""
agent = self.agents[task_name]
replay_buffer = self.replay_buffers[task_name]
# Sample batch from replay buffer
batch_size = min(32, len(replay_buffer))
batch_indices = random.sample(range(len(replay_buffer)), batch_size)
batch = [replay_buffer[i] for i in batch_indices]
# Train agent with batch
loss = agent.update(batch)
return {
'loss': loss,
'samples_used': batch_size,
'success': loss is not None
}
class TransferLearner:
"""Transfer learning for knowledge transfer between tasks"""
def __init__(self):
self.knowledge_base = {}
self.transfer_strategies = {
'feature_extraction': self.feature_extraction_transfer,
'fine_tuning': self.fine_tuning_transfer,
'multi_task': self.multi_task_transfer
}
def update(self, experience: Dict, objective: str) -> Dict:
"""Update with transfer learning"""
# Identify source and target tasks
source_task = experience.get('source_task')
target_task = experience.get('target_task', 'current')
similarity = experience.get('task_similarity', 0.0)
if source_task and similarity > 0.3: # Significant similarity
# Apply transfer strategy based on similarity
if similarity > 0.7:
strategy = 'fine_tuning'
elif similarity > 0.5:
strategy = 'feature_extraction'
else:
strategy = 'multi_task'
transfer_result = self.transfer_strategies[strategy](
source_task, target_task, experience
)
return {
'success': True,
'strategy': strategy,
'transfer_result': transfer_result,
'similarity': similarity
}
return {'success': False, 'reason': 'no_similar_tasks'}
def feature_extraction_transfer(self, source_task: str, target_task: str, experience: Dict) -> Dict:
"""Transfer learned features from source to target task"""
# This would copy learned features from source model to target
return {'features_transferred': True, 'accuracy_improvement': 0.1}
def fine_tuning_transfer(self, source_task: str, target_task: str, experience: Dict) -> Dict:
"""Fine-tune target model using source model weights"""
# This would initialize target model with source weights and fine-tune
return {'fine_tuned': True, 'convergence_speed': 0.8}
def multi_task_transfer(self, source_task: str, target_task: str, experience: Dict) -> Dict:
"""Multi-task learning combining source and target tasks"""
# This would create shared representations for both tasks
return {'multi_task_learned': True, 'shared_features': 5}
class MetaLearner:
"""Meta-learning for rapid adaptation to new tasks"""
def __init__(self):
self.meta_model = MetaLearningModel()
self.task_sampler = TaskSampler()
self.adaptation_network = AdaptationNetwork()
def update(self, experience: Dict, objective: str) -> Dict:
"""Update meta-learning system"""
# Sample tasks for meta-training
tasks = self.task_sampler.sample_related_tasks(experience)
# Update meta-model with task distribution
meta_update_result = self.meta_model.update_with_tasks(tasks)
# Update adaptation network
adaptation_result = self.adaptation_network.update(
experience, tasks
)
return {
'success': True,
'meta_update': meta_update_result,
'adaptation_result': adaptation_result,
'tasks_sampled': len(tasks),
'adaptation_speed': adaptation_result.get('speed', 0.0)
}
class MemorySystem:
"""Episodic and semantic memory for the AI-Robot Brain"""
def __init__(self):
self.episodic_memory = deque(maxlen=10000) # Recent experiences
self.semantic_memory = {} # General knowledge
self.working_memory = {} # Current context
self.memory_index = {} # Fast retrieval index
def store_experience(self, experience: Dict):
"""Store experience in memory"""
# Add timestamp
experience['timestamp'] = time.time()
# Store in episodic memory
self.episodic_memory.append(experience)
# Update semantic memory with learned patterns
self.update_semantic_memory(experience)
# Update memory index for fast retrieval
self.update_memory_index(experience)
def retrieve_relevant(self, query: Dict, k: int = 5) -> List[Dict]:
"""Retrieve relevant experiences from memory"""
# Use memory index for fast retrieval
relevant_indices = self.find_relevant_indices(query, k)
# Retrieve experiences
relevant_experiences = []
for idx in relevant_indices:
if idx < len(self.episodic_memory):
relevant_experiences.append(self.episodic_memory[idx])
return relevant_experiences
def update_semantic_memory(self, experience: Dict):
"""Update semantic memory with learned patterns"""
# Extract patterns and generalizations from experience
patterns = self.extract_patterns(experience)
for pattern_key, pattern_value in patterns.items():
if pattern_key in self.semantic_memory:
# Update existing pattern
self.semantic_memory[pattern_key] = self.update_pattern(
self.semantic_memory[pattern_key], pattern_value
)
else:
# Add new pattern
self.semantic_memory[pattern_key] = pattern_value
def extract_patterns(self, experience: Dict) -> Dict:
"""Extract semantic patterns from experience"""
# This would identify recurring patterns and generalizations
return {'pattern_key': 'pattern_value'} # Placeholder
def update_pattern(self, old_pattern: Any, new_pattern: Any) -> Any:
"""Update existing pattern with new information"""
# This would implement pattern updating logic
return new_pattern
def update_memory_index(self, experience: Dict):
"""Update memory index for fast retrieval"""
# Create index based on experience content
index_key = self.create_index_key(experience)
timestamp = experience['timestamp']
if index_key not in self.memory_index:
self.memory_index[index_key] = []
self.memory_index[index_key].append(timestamp)
def create_index_key(self, experience: Dict) -> str:
"""Create index key for experience"""
# This would create meaningful index keys
return 'default_key'
def find_relevant_indices(self, query: Dict, k: int) -> List[int]:
"""Find indices of relevant experiences"""
# This would implement similarity search
return list(range(min(k, len(self.episodic_memory))))
class ExperienceReplay:
"""Experience replay for memory consolidation"""
def __init__(self):
self.replay_buffer = deque(maxlen=50000)
self.prioritized_replay = True
self.replay_probability = 0.1 # 10% chance of replay
def consolidate(self):
"""Consolidate experiences in memory"""
# This would implement memory consolidation strategies
pass
def sample_replay_batch(self, batch_size: int = 32) -> List[Dict]:
"""Sample experiences for replay"""
if len(self.replay_buffer) < batch_size:
return list(self.replay_buffer)
# Sample randomly or with priority
if self.prioritized_replay:
# Implement prioritized sampling
indices = random.sample(range(len(self.replay_buffer)), batch_size)
else:
indices = random.sample(range(len(self.replay_buffer)), batch_size)
return [self.replay_buffer[i] for i in indices]
class PerformanceAnalyzer:
"""Analyze learning performance and adaptation"""
def __init__(self):
self.performance_history = {}
self.improvement_metrics = {}
self.bottleneck_detector = BottleneckDetector()
def analyze(self, learning_results: Dict, performance_feedback: Dict) -> Dict:
"""Analyze learning performance"""
# Calculate improvement metrics
improvement_rate = self.calculate_improvement_rate(
learning_results, performance_feedback
)
# Detect learning bottlenecks
bottlenecks = self.bottleneck_detector.detect(
learning_results, performance_feedback
)
# Update performance history
self.update_performance_history(learning_results, improvement_rate)
return {
'improvement_rate': improvement_rate,
'bottlenecks': bottlenecks,
'performance_trend': self.analyze_trend(),
'learning_efficiency': self.calculate_efficiency(learning_results),
'adaptation_quality': self.calculate_adaptation_quality(performance_feedback)
}
def calculate_improvement_rate(self, results: Dict, feedback: Dict) -> float:
"""Calculate learning improvement rate"""
# This would compare before/after performance
return 0.05 # Placeholder
def update_performance_history(self, results: Dict, improvement: float):
"""Update performance history"""
current_time = time.time()
self.performance_history[current_time] = {
'results': results,
'improvement': improvement,
'timestamp': current_time
}
def analyze_trend(self) -> str:
"""Analyze learning trend"""
# This would analyze historical performance data
return 'improving'
def calculate_efficiency(self, results: Dict) -> float:
"""Calculate learning efficiency"""
# This would measure learning per unit time/resource
return 0.7 # Placeholder
def calculate_adaptation_quality(self, feedback: Dict) -> float:
"""Calculate adaptation quality"""
# This would measure how well adaptation improved performance
return 0.8 # Placeholder
class MotionPlanningNet(nn.Module):
"""Neural network for motion planning tasks"""
def __init__(self):
super().__init__()
self.encoder = nn.Sequential(
nn.Linear(12, 64), # Input: joint states, obstacles, goal
nn.ReLU(),
nn.Linear(64, 128),
nn.ReLU(),
nn.Linear(128, 64),
nn.ReLU()
)
self.decoder = nn.Sequential(
nn.Linear(64, 32),
nn.ReLU(),
nn.Linear(32, 12) # Output: next joint positions
)
def forward(self, x):
encoded = self.encoder(x)
return self.decoder(encoded)
class ObjectRecognitionNet(nn.Module):
"""Neural network for object recognition"""
def __init__(self):
super().__init__()
self.conv_layers = nn.Sequential(
nn.Conv2d(3, 32, 3, padding=1),
nn.ReLU(),
nn.Conv2d(32, 64, 3, padding=1),
nn.ReLU(),
nn.AdaptiveAvgPool2d((4, 4))
)
self.classifier = nn.Sequential(
nn.Linear(64 * 4 * 4, 128),
nn.ReLU(),
nn.Linear(128, 10) # 10 object classes
)
def forward(self, x):
features = self.conv_layers(x)
flattened = features.view(features.size(0), -1)
return self.classifier(flattened)
class GraspPredictionNet(nn.Module):
"""Neural network for grasp prediction"""
def __init__(self):
super().__init__()
self.feature_extractor = nn.Sequential(
nn.Linear(6, 32), # Object pose and shape
nn.ReLU(),
nn.Linear(32, 64),
nn.ReLU()
)
self.grasp_predictor = nn.Sequential(
nn.Linear(64, 32),
nn.ReLU(),
nn.Linear(32, 4) # Grasp pose: position + orientation
)
def forward(self, x):
features = self.feature_extractor(x)
return self.grasp_predictor(features)
class GeneralNet(nn.Module):
"""General neural network"""
def __init__(self):
super().__init__()
self.layers = nn.Sequential(
nn.Linear(10, 32),
nn.ReLU(),
nn.Linear(32, 16),
nn.ReLU(),
nn.Linear(16, 1)
)
def forward(self, x):
return self.layers(x)
class NavigationDQNAgent:
"""DQN agent for navigation tasks"""
def __init__(self):
self.q_network = nn.Sequential(
nn.Linear(24, 128), # State: position, obstacles, goal
nn.ReLU(),
nn.Linear(128, 64),
nn.ReLU(),
nn.Linear(64, 4) # Actions: move in 4 directions
)
self.optimizer = torch.optim.Adam(self.q_network.parameters())
def update(self, batch):
"""Update DQN with experience batch"""
states = torch.tensor([exp['state'] for exp in batch], dtype=torch.float32)
actions = torch.tensor([exp['action'] for exp in batch], dtype=torch.long)
rewards = torch.tensor([exp['reward'] for exp in batch], dtype=torch.float32)
q_values = self.q_network(states)
action_q_values = q_values.gather(1, actions.unsqueeze(1))
loss = nn.MSELoss()(action_q_values.squeeze(), rewards)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
return loss.item()
class ManipulationPPOAgent:
"""PPO agent for manipulation tasks"""
def __init__(self):
pass
def update(self, batch):
"""Update PPO agent"""
return 0.0 # Placeholder
class LocomotionSACAgent:
"""SAC agent for locomotion tasks"""
def __init__(self):
pass
def update(self, batch):
"""Update SAC agent"""
return 0.0 # Placeholder
class GeneralRLAgent:
"""General RL agent"""
def __init__(self):
pass
def update(self, batch):
"""Update general RL agent"""
return 0.0 # Placeholder
class MetaLearningModel:
"""Model for meta-learning"""
def __init__(self):
pass
def update_with_tasks(self, tasks: List[Dict]) -> Dict:
"""Update with multiple tasks"""
return {'success': True, 'tasks_processed': len(tasks)}
class TaskSampler:
"""Sample related tasks for meta-learning"""
def __init__(self):
pass
def sample_related_tasks(self, experience: Dict) -> List[Dict]:
"""Sample tasks related to current experience"""
return [experience] # Placeholder
class AdaptationNetwork(nn.Module):
"""Network for rapid adaptation"""
def __init__(self):
super().__init__()
self.adaptation_layer = nn.Linear(10, 10) # Placeholder
def forward(self, x):
return self.adaptation_layer(x)
def update(self, experience: Dict, tasks: List[Dict]) -> Dict:
"""Update adaptation network"""
return {'success': True, 'adaptation_applied': True}
class BottleneckDetector:
"""Detect learning bottlenecks"""
def __init__(self):
pass
def detect(self, learning_results: Dict, performance_feedback: Dict) -> List[str]:
"""Detect learning bottlenecks"""
bottlenecks = []
# Check for common bottlenecks
if learning_results.get('convergence_rate', 1.0) < 0.1:
bottlenecks.append('slow_convergence')
if performance_feedback.get('error_rate', 0.0) > 0.5:
bottlenecks.append('high_error_rate')
return bottlenecks
Real-Time Scheduling and Task Orchestration
Priority-Based Task Management
The AI-Robot Brain must manage multiple concurrent tasks with different priorities and deadlines. This section implements sophisticated scheduling and orchestration mechanisms.
import asyncio
import threading
from concurrent.futures import ThreadPoolExecutor, as_completed
import heapq
from typing import Dict, List, Any, Callable, Optional
import time
class TaskScheduler:
"""Advanced task scheduler for the AI-Robot Brain"""
def __init__(self):
self.real_time_scheduler = RealTimeScheduler()
self.priority_queue = PriorityQueue()
self.resource_manager = ResourceManager()
self.task_monitor = TaskMonitor()
self.executor = ThreadPoolExecutor(max_workers=8)
def schedule_cognitive_task(self, task: CognitiveTask) -> str:
"""Schedule a cognitive task with appropriate priority and timing"""
# Validate task
if not self.validate_task(task):
raise ValueError(f"Invalid task: {task.id}")
# Determine execution strategy based on task characteristics
if self.is_real_time_critical(task):
return self.real_time_scheduler.schedule(task)
else:
return self.priority_queue.enqueue(task)
def validate_task(self, task: CognitiveTask) -> bool:
"""Validate task parameters"""
if not task.id or not task.module_type:
return False
if not (0 <= task.priority <= 10):
return False
return True
def is_real_time_critical(self, task: CognitiveTask) -> bool:
"""Determine if task requires real-time execution"""
# Safety-critical tasks and control tasks are real-time critical
if task.module_type in [CognitiveModuleType.CONTROL, CognitiveModuleType.SAFETY]:
return True
# Tasks with very short deadlines are real-time critical
if (task.deadline - time.time()) < 0.01: # 10ms deadline
return True
return False
def execute_scheduled_tasks(self) -> List[Dict]:
"""Execute all scheduled tasks according to their timing"""
results = []
# Execute real-time tasks
rt_results = self.real_time_scheduler.execute_ready_tasks()
results.extend(rt_results)
# Execute priority queue tasks
pq_results = self.priority_queue.execute_ready_tasks()
results.extend(pq_results)
return results
class RealTimeScheduler:
"""Scheduler for real-time critical tasks"""
def __init__(self):
self.task_queue = [] # Min-heap based on deadline
self.running = False
self.scheduler_thread = None
def schedule(self, task: CognitiveTask) -> str:
"""Schedule a real-time task"""
# Add to heap with deadline as priority
heapq.heappush(self.task_queue, (task.deadline, task.id, task))
return task.id
def execute_ready_tasks(self) -> List[Dict]:
"""Execute tasks that are ready and past their deadline"""
results = []
current_time = time.time()
# Execute all tasks that are ready
while self.task_queue and self.task_queue[0][0] <= current_time:
deadline, task_id, task = heapq.heappop(self.task_queue)
try:
result = task.callback(task.data) if task.callback else None
results.append({
'task_id': task_id,
'result': result,
'status': 'completed',
'execution_time': time.time() - deadline
})
except Exception as e:
results.append({
'task_id': task_id,
'error': str(e),
'status': 'failed'
})
return results
class PriorityQueue:
"""Priority queue for non-real-time tasks"""
def __init__(self):
self.queue = [] # Max-heap based on priority (negative for max-heap)
self.task_map = {} # task_id -> task
def enqueue(self, task: CognitiveTask) -> str:
"""Add task to priority queue"""
# Use negative priority for max-heap behavior
priority_key = (-task.priority, time.time(), task.id)
heapq.heappush(self.queue, (priority_key, task))
self.task_map[task.id] = task
return task.id
def execute_ready_tasks(self) -> List[Dict]:
"""Execute highest priority ready tasks"""
results = []
if self.queue:
priority_key, task = heapq.heappop(self.queue)
task_id = task.id
try:
result = task.callback(task.data) if task.callback else None
results.append({
'task_id': task_id,
'result': result,
'status': 'completed'
})
except Exception as e:
results.append({
'task_id': task_id,
'error': str(e),
'status': 'failed'
})
return results
class TaskMonitor:
"""Monitor task execution and performance"""
def __init__(self):
self.task_history = {}
self.performance_metrics = {}
def log_task_execution(self, task_id: str, start_time: float, end_time: float, result: Any):
"""Log task execution details"""
execution_time = end_time - start_time
self.task_history[task_id] = {
'start_time': start_time,
'end_time': end_time,
'execution_time': execution_time,
'result': result,
'timestamp': time.time()
}
def get_performance_metrics(self) -> Dict:
"""Get performance metrics for task execution"""
if not self.task_history:
return {}
execution_times = [record['execution_time'] for record in self.task_history.values()]
return {
'avg_execution_time': sum(execution_times) / len(execution_times),
'max_execution_time': max(execution_times),
'min_execution_time': min(execution_times),
'total_tasks_executed': len(self.task_history)
}
class CognitiveOrchestrator:
"""Orchestrate cognitive tasks across the AI-Robot Brain"""
def __init__(self, brain_core: AIBrainCore):
self.brain = brain_core
self.workflow_manager = WorkflowManager()
self.dependency_resolver = DependencyResolver()
self.completion_callbacks = {}
def execute_workflow(self, workflow: Dict[str, Any]) -> str:
"""Execute a cognitive workflow"""
# Parse workflow and resolve dependencies
tasks = self.workflow_manager.parse_workflow(workflow)
resolved_tasks = self.dependency_resolver.resolve_dependencies(tasks)
# Schedule all tasks
workflow_id = f"workflow_{int(time.time())}"
for task_spec in resolved_tasks:
task = CognitiveTask(
id=f"{workflow_id}_{task_spec['id']}",
module_type=CognitiveModuleType(task_spec['module']),
priority=task_spec.get('priority', 5),
deadline=time.time() + task_spec.get('timeout', 10.0),
data=task_spec['data'],
callback=self.create_completion_callback(workflow_id, task_spec['id'])
)
self.brain.schedule_task(task)
return workflow_id
def create_completion_callback(self, workflow_id: str, task_id: str) -> Callable:
"""Create callback for task completion"""
def callback(result):
if workflow_id not in self.completion_callbacks:
self.completion_callbacks[workflow_id] = {}
self.completion_callbacks[workflow_id][task_id] = result
return callback
def get_workflow_status(self, workflow_id: str) -> Dict:
"""Get status of a workflow"""
if workflow_id in self.completion_callbacks:
completed_tasks = len(self.completion_callbacks[workflow_id])
total_tasks = len(self.get_workflow_tasks(workflow_id))
return {
'workflow_id': workflow_id,
'completed_tasks': completed_tasks,
'total_tasks': total_tasks,
'progress': completed_tasks / total_tasks if total_tasks > 0 else 0,
'status': 'completed' if completed_tasks == total_tasks else 'in_progress'
}
return {'status': 'not_found'}
def get_workflow_tasks(self, workflow_id: str) -> List[str]:
"""Get list of tasks in a workflow"""
# This would retrieve task list from workflow definition
return [] # Placeholder
class WorkflowManager:
"""Manage cognitive workflows"""
def __init__(self):
self.workflows = {}
self.workflow_templates = {}
def parse_workflow(self, workflow_spec: Dict) -> List[Dict]:
"""Parse workflow specification into executable tasks"""
tasks = []
for step in workflow_spec.get('steps', []):
task = {
'id': step['id'],
'module': step['module'],
'data': step.get('data', {}),
'dependencies': step.get('dependencies', []),
'priority': step.get('priority', 5),
'timeout': step.get('timeout', 10.0)
}
tasks.append(task)
return tasks
def register_workflow_template(self, name: str, template: Dict):
"""Register a workflow template"""
self.workflow_templates[name] = template
def instantiate_workflow(self, template_name: str, parameters: Dict) -> Dict:
"""Instantiate workflow from template with parameters"""
if template_name not in self.workflow_templates:
raise ValueError(f"Template {template_name} not found")
template = self.workflow_templates[template_name]
# Apply parameters to template (simplified)
return template
class DependencyResolver:
"""Resolve dependencies between cognitive tasks"""
def __init__(self):
self.dependency_graph = {}
def resolve_dependencies(self, tasks: List[Dict]) -> List[Dict]:
"""Resolve task dependencies and return execution order"""
# Build dependency graph
graph = self.build_dependency_graph(tasks)
# Topological sort to get execution order
execution_order = self.topological_sort(graph)
# Return tasks in execution order
ordered_tasks = []
for task_id in execution_order:
task = next((t for t in tasks if t['id'] == task_id), None)
if task:
ordered_tasks.append(task)
return ordered_tasks
def build_dependency_graph(self, tasks: List[Dict]) -> Dict:
"""Build dependency graph from tasks"""
graph = {task['id']: set() for task in tasks}
for task in tasks:
for dep in task.get('dependencies', []):
if dep in graph:
graph[dep].add(task['id']) # dep -> task dependency
return graph
def topological_sort(self, graph: Dict[str, set]) -> List[str]:
"""Perform topological sort on dependency graph"""
# Kahn's algorithm for topological sorting
in_degree = {node: 0 for node in graph}
for node in graph:
for neighbor in graph[node]:
in_degree[neighbor] += 1
queue = [node for node in graph if in_degree[node] == 0]
result = []
while queue:
node = queue.pop(0)
result.append(node)
for neighbor in graph[node]:
in_degree[neighbor] -= 1
if in_degree[neighbor] == 0:
queue.append(neighbor)
return result
class ResourceScheduler:
"""Schedule resources across cognitive modules"""
def __init__(self):
self.resource_allocations = {}
self.resource_usage = {}
self.resource_limits = {
'cpu': 1.0, # 100% of CPU
'gpu': 1.0, # 100% of GPU
'memory': 1.0, # 100% of memory
'bandwidth': 1.0 # 100% of bandwidth
}
def allocate_resources(self, tasks: List[CognitiveTask]) -> Dict:
"""Allocate resources to tasks based on priority and requirements"""
allocations = {}
for task in tasks:
# Determine resource requirements based on task type and priority
cpu_req = self.estimate_cpu_requirement(task)
gpu_req = self.estimate_gpu_requirement(task)
mem_req = self.estimate_memory_requirement(task)
allocations[task.id] = {
'cpu': min(cpu_req, self.resource_limits['cpu']),
'gpu': min(gpu_req, self.resource_limits['gpu']),
'memory': min(mem_req, self.resource_limits['memory']),
'bandwidth': 0.1 # Default bandwidth allocation
}
return allocations
def estimate_cpu_requirement(self, task: CognitiveTask) -> float:
"""Estimate CPU requirement for task"""
base_requirement = 0.1 # Base requirement
if task.module_type == CognitiveModuleType.PERCEPTION:
return base_requirement * 0.3 # Heavy computation
elif task.module_type == CognitiveModuleType.CONTROL:
return base_requirement * 0.2 # Real-time constraints
elif task.module_type == CognitiveModuleType.LEARNING:
return base_requirement * 0.4 # Training intensive
else:
return base_requirement
def estimate_gpu_requirement(self, task: CognitiveTask) -> float:
"""Estimate GPU requirement for task"""
if task.module_type in [CognitiveModuleType.PERCEPTION, CognitiveModuleType.LEARNING]:
return 0.3 # These modules benefit from GPU
return 0.05 # Minimal GPU usage
def estimate_memory_requirement(self, task: CognitiveTask) -> float:
"""Estimate memory requirement for task"""
base_requirement = 0.05
if task.module_type == CognitiveModuleType.MEMORY:
return base_requirement * 2.0 # Memory-intensive
elif task.module_type == CognitiveModuleType.PERCEPTION:
return base_requirement * 1.5 # Large data processing
else:
return base_requirement
def monitor_resource_usage(self) -> Dict:
"""Monitor current resource usage"""
return self.resource_usage
def get_resource_for_task(self, task_id: str, resource_type: str) -> float:
"""Get allocated resource amount for a task"""
if task_id in self.resource_allocations:
return self.resource_allocations[task_id].get(resource_type, 0.0)
return 0.0
NVIDIA Hardware Acceleration Integration
Optimizing for NVIDIA GPUs and Tensor Cores
The AI-Robot Brain leverages NVIDIA's specialized hardware for maximum performance. This section implements optimizations for GPU acceleration and Tensor Core usage.
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.cpp_extension import load
import tensorrt as trt
import pycuda.driver as cuda
import pycuda.autoinit
import numpy as np
from typing import Dict, List, Any, Optional
import time
class NVIDIACoreAccelerator:
"""Core acceleration system for NVIDIA hardware"""
def __init__(self):
self.gpu_manager = GPUManager()
self.tensorrt_manager = TensorRTManager()
self.cudagraph_manager = CUDAGraphManager()
self.optimization_strategies = self.define_optimization_strategies()
def optimize_for_hardware(self, module: nn.Module, input_shapes: List[tuple]) -> nn.Module:
"""Optimize neural network module for NVIDIA hardware"""
# Apply tensor core optimizations
optimized_module = self.optimize_for_tensor_cores(module)
# Convert to TensorRT if beneficial
if self.should_use_tensorrt(module, input_shapes):
optimized_module = self.tensorrt_manager.optimize(optimized_module, input_shapes)
# Apply mixed precision if supported
optimized_module = self.apply_mixed_precision(optimized_module)
return optimized_module
def optimize_for_tensor_cores(self, module: nn.Module) -> nn.Module:
"""Optimize module to take advantage of Tensor Cores"""
# Ensure layer dimensions are multiples of 8 for optimal Tensor Core usage
for name, layer in module.named_modules():
if isinstance(layer, (nn.Linear, nn.Conv2d)):
# Adjust dimensions to be multiples of 8 where possible
if hasattr(layer, 'weight'):
weight = layer.weight
if len(weight.shape) >= 2:
# Ensure channel dimensions are multiples of 8
if weight.shape[0] % 8 != 0 or weight.shape[1] % 8 != 0:
# This is a simplified approach - in practice, you might need to
# pad or adjust the network architecture
pass
return module
def should_use_tensorrt(self, module: nn.Module, input_shapes: List[tuple]) -> bool:
"""Determine if TensorRT optimization is beneficial"""
# Check if module has operations that benefit from TensorRT
has_conv = any(isinstance(layer, nn.Conv2d) for layer in module.modules())
has_linear = any(isinstance(layer, nn.Linear) for layer in module.modules())
has_relu = any(isinstance(layer, nn.ReLU) for layer in module.modules())
# TensorRT is beneficial for models with these operations and sufficient size
return (has_conv or has_linear) and sum(p.numel() for p in module.parameters()) > 1000
def apply_mixed_precision(self, module: nn.Module) -> nn.Module:
"""Apply mixed precision optimization"""
# Use PyTorch's automatic mixed precision
from torch.cuda.amp import GradScaler, autocast
# Mark the module as ready for mixed precision
module.half() # Convert to FP16 where appropriate
return module
def execute_with_optimization(self, module: nn.Module, inputs: List[torch.Tensor]) -> List[torch.Tensor]:
"""Execute module with hardware optimization"""
# Move inputs to GPU
gpu_inputs = [inp.cuda() for inp in inputs]
# Use CUDA graphs for repeated execution
if self.cudagraph_manager.is_suitable_for_cudagraph(module):
return self.cudagraph_manager.execute_with_graph(module, gpu_inputs)
# Standard optimized execution
with torch.cuda.amp.autocast():
with torch.no_grad():
outputs = module(*gpu_inputs)
return [outputs] if not isinstance(outputs, (list, tuple)) else list(outputs)
def define_optimization_strategies(self) -> Dict[str, Callable]:
"""Define optimization strategies for different modules"""
return {
'perception': self.optimize_perception_module,
'planning': self.optimize_planning_module,
'learning': self.optimize_learning_module,
'control': self.optimize_control_module
}
def optimize_perception_module(self, module: nn.Module) -> nn.Module:
"""Optimize perception module for GPU"""
# Perception modules often benefit from TensorRT optimization
return self.tensorrt_manager.optimize(module, [(1, 3, 480, 640)]) # Example input shape
def optimize_planning_module(self, module: nn.Module) -> nn.Module:
"""Optimize planning module for GPU"""
# Planning modules may need different optimization strategies
return self.optimize_for_tensor_cores(module)
def optimize_learning_module(self, module: nn.Module) -> nn.Module:
"""Optimize learning module for GPU"""
# Learning modules benefit from mixed precision and Tensor Core optimization
module = self.apply_mixed_precision(module)
return self.optimize_for_tensor_cores(module)
def optimize_control_module(self, module: nn.Module) -> nn.Module:
"""Optimize control module for GPU"""
# Control modules need low latency, so optimize for speed
return self.cudagraph_manager.optimize_for_low_latency(module)
class GPUManager:
"""Manage GPU resources and optimization"""
def __init__(self):
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
self.gpu_memory_manager = GPUMemoryManager()
self.multi_gpu_manager = MultiGPUManager()
def get_available_gpus(self) -> List[int]:
"""Get list of available GPUs"""
if torch.cuda.is_available():
return list(range(torch.cuda.device_count()))
return []
def allocate_gpu_memory(self, size_mb: int) -> bool:
"""Allocate GPU memory"""
return self.gpu_memory_manager.allocate(size_mb)
def distribute_model(self, model: nn.Module) -> nn.Module:
"""Distribute model across multiple GPUs if available"""
available_gpus = self.get_available_gpus()
if len(available_gpus) > 1:
return self.multi_gpu_manager.distribute_model(model, available_gpus)
else:
return model.to(self.device)
def monitor_gpu_usage(self) -> Dict[str, Any]:
"""Monitor GPU usage and performance"""
if torch.cuda.is_available():
gpu_stats = {}
for i in range(torch.cuda.device_count()):
gpu_stats[f'gpu_{i}'] = {
'memory_allocated': torch.cuda.memory_allocated(i),
'memory_reserved': torch.cuda.memory_reserved(i),
'utilization': torch.cuda.utilization(i) if hasattr(torch.cuda, 'utilization') else 0
}
return gpu_stats
return {}
class GPUMemoryManager:
"""Manage GPU memory allocation and optimization"""
def __init__(self):
self.allocated_memory = 0
self.memory_pool = {}
def allocate(self, size_mb: int) -> bool:
"""Allocate GPU memory"""
if self._has_sufficient_memory(size_mb):
self.allocated_memory += size_mb
return True
return False
def _has_sufficient_memory(self, size_mb: int) -> bool:
"""Check if sufficient GPU memory is available"""
if torch.cuda.is_available():
total_memory = torch.cuda.get_device_properties(0).total_memory / (1024**2) # MB
allocated = torch.cuda.memory_allocated(0) / (1024**2) # MB
reserved = torch.cuda.memory_reserved(0) / (1024**2) # MB
available = total_memory - reserved
return available >= size_mb
return False
def optimize_memory_layout(self, tensor: torch.Tensor) -> torch.Tensor:
"""Optimize tensor memory layout for GPU"""
# Ensure tensor is contiguous and properly aligned
if not tensor.is_contiguous():
tensor = tensor.contiguous()
# Pad dimensions to multiples of 8 for Tensor Core efficiency
if len(tensor.shape) >= 2:
shape = list(tensor.shape)
if shape[-1] % 8 != 0:
pad_size = 8 - (shape[-1] % 8)
shape[-1] += pad_size
tensor = F.pad(tensor, (0, pad_size))
return tensor
class MultiGPUManager:
"""Manage multi-GPU execution"""
def __init__(self):
self.num_gpus = torch.cuda.device_count() if torch.cuda.is_available() else 0
def distribute_model(self, model: nn.Module, gpu_ids: List[int]) -> nn.Module:
"""Distribute model across multiple GPUs"""
if self.num_gpus > 1 and len(gpu_ids) > 1:
# Use DataParallel for simple multi-GPU distribution
return nn.DataParallel(model, device_ids=gpu_ids)
else:
# Use single GPU or CPU
device = torch.device(f'cuda:{gpu_ids[0]}' if gpu_ids else 'cpu')
return model.to(device)
def distribute_data(self, data: torch.Tensor, gpu_ids: List[int]) -> List[torch.Tensor]:
"""Distribute data across multiple GPUs"""
if len(gpu_ids) <= 1:
return [data.cuda(gpu_ids[0]) if gpu_ids else data]
# Split data across GPUs
batch_size = data.size(0)
chunk_size = batch_size // len(gpu_ids)
chunks = []
for i, gpu_id in enumerate(gpu_ids):
start_idx = i * chunk_size
end_idx = (i + 1) * chunk_size if i < len(gpu_ids) - 1 else batch_size
chunk = data[start_idx:end_idx].cuda(gpu_id)
chunks.append(chunk)
return chunks
class TensorRTManager:
"""Manage TensorRT optimization"""
def __init__(self):
self.tensorrt_engines = {}
self.optimization_cache = {}
def optimize(self, module: nn.Module, input_shapes: List[tuple]) -> nn.Module:
"""Optimize module using TensorRT"""
# Convert PyTorch model to ONNX first
onnx_model = self.pytorch_to_onnx(module, input_shapes)
# Convert ONNX to TensorRT engine
engine = self.onnx_to_tensorrt(onnx_model, input_shapes)
# Create TensorRT inference wrapper
return TensorRTInference(engine)
def pytorch_to_onnx(self, model: nn.Module, input_shapes: List[tuple]) -> str:
"""Convert PyTorch model to ONNX format"""
import io
# Create dummy inputs based on shapes
dummy_inputs = []
for shape in input_shapes:
dummy_input = torch.randn(shape)
dummy_inputs.append(dummy_input)
# If single input, don't wrap in list
if len(dummy_inputs) == 1:
dummy_input = dummy_inputs[0]
else:
dummy_input = tuple(dummy_inputs)
# Export to ONNX
onnx_buffer = io.BytesIO()
torch.onnx.export(
model,
dummy_input,
onnx_buffer,
export_params=True,
opset_version=11,
do_constant_folding=True,
input_names=[f'input_{i}' for i in range(len(input_shapes))],
output_names=['output']
)
return onnx_buffer.getvalue()
def onnx_to_tensorrt(self, onnx_model: bytes, input_shapes: List[tuple]) -> Any:
"""Convert ONNX model to TensorRT engine"""
# This is a simplified version - in practice, you'd use TensorRT Python API
# to build the engine properly
# Create TensorRT builder and network
TRT_LOGGER = trt.Logger(trt.Logger.WARNING)
builder = trt.Builder(TRT_LOGGER)
network = builder.create_network(1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))
parser = trt.OnnxParser(network, TRT_LOGGER)
# Parse ONNX model
if not parser.parse(onnx_model):
for error in range(parser.num_errors):
print(parser.get_error(error))
# Configure builder
config = builder.create_builder_config()
config.max_workspace_size = 1 << 30 # 1GB
# Build engine
serialized_engine = builder.build_serialized_network(network, config)
# Create runtime and engine
runtime = trt.Runtime(TRT_LOGGER)
engine = runtime.deserialize_cuda_engine(serialized_engine)
return engine
class TensorRTInference:
"""TensorRT inference wrapper"""
def __init__(self, engine):
self.engine = engine
self.context = engine.create_execution_context()
# Allocate I/O buffers
self.inputs = []
self.outputs = []
self.bindings = []
self.stream = cuda.Stream()
for binding in engine:
size = trt.volume(engine.get_binding_shape(binding)) * engine.max_batch_size * 4 # in bytes
host_mem = cuda.pagelocked_empty(size, dtype=np.float32)
cuda_mem = cuda.mem_alloc(host_mem.nbytes)
self.bindings.append(int(cuda_mem))
if engine.binding_is_input(binding):
self.inputs.append({'host': host_mem, 'device': cuda_mem})
else:
self.outputs.append({'host': host_mem, 'device': cuda_mem})
def __call__(self, *inputs):
# Transfer input data to device
for i, inp in enumerate(inputs):
if isinstance(inp, torch.Tensor):
inp = inp.cpu().numpy()
np.copyto(self.inputs[i]['host'], inp.ravel())
cuda.memcpy_htod_async(self.inputs[i]['device'], self.inputs[i]['host'], self.stream)
# Run inference
self.context.execute_async_v2(bindings=self.bindings, stream_handle=self.stream.handle)
# Transfer predictions back
for out in self.outputs:
cuda.memcpy_dtoh_async(out['host'], out['device'], self.stream)
self.stream.synchronize()
# Reshape and return outputs
results = []
for out in self.outputs:
output = out['host']
results.append(torch.from_numpy(output))
return results[0] if len(results) == 1 else results
class CUDAGraphManager:
"""Manage CUDA graphs for optimized execution"""
def __init__(self):
self.graphs = {}
self.graph_inputs = {}
def execute_with_graph(self, module: nn.Module, inputs: List[torch.Tensor]) -> List[torch.Tensor]:
"""Execute with CUDA graph for optimized performance"""
# Create graph key based on module and input characteristics
graph_key = self._create_graph_key(module, inputs)
if graph_key not in self.graphs:
# Capture new graph
self.graphs[graph_key] = self._capture_graph(module, inputs)
self.graph_inputs[graph_key] = inputs
# Replay graph
graph = self.graphs[graph_key]
graph.replay()
return self.graph_inputs[graph_key] # Updated inputs/outputs
def _create_graph_key(self, module: nn.Module, inputs: List[torch.Tensor]) -> str:
"""Create unique key for graph caching"""
import hashlib
# Create hash based on module structure and input shapes
module_str = str([type(layer).__name__ for layer in module.modules()])
input_shapes = str([inp.shape for inp in inputs])
key_str = f"{module_str}_{input_shapes}"
return hashlib.md5(key_str.encode()).hexdigest()
def _capture_graph(self, module: nn.Module, inputs: List[torch.Tensor]) -> torch.cuda.CUDAGraph:
"""Capture CUDA graph for the module"""
# Warm up
for _ in range(3):
_ = module(*inputs)
# Create and capture graph
graph = torch.cuda.CUDAGraph()
with torch.cuda.graph(graph):
outputs = module(*inputs)
return graph
def is_suitable_for_cudagraph(self, module: nn.Module) -> bool:
"""Check if module is suitable for CUDA graph optimization"""
# CUDA graphs work best with modules that have consistent execution patterns
# and don't have control flow that changes between calls
return True # Simplified check
def optimize_for_low_latency(self, module: nn.Module) -> nn.Module:
"""Optimize module for low latency execution"""
# This would apply specific optimizations for low-latency scenarios
return module
System Integration and Validation
Comprehensive System Testing
The final section covers the integration of all components and comprehensive validation of the AI-Robot Brain architecture.
import unittest
import time
from typing import Dict, List, Any
import threading
import queue
class AIIntegrationTestSuite:
"""Comprehensive test suite for AI-Robot Brain integration"""
def __init__(self, brain_core: AIBrainCore):
self.brain = brain_core
self.test_results = {}
self.performance_benchmarks = {}
self.integration_tests = [
self.test_perception_integration,
self.test_planning_integration,
self.test_control_integration,
self.test_learning_integration,
self.test_multi_module_coordination,
self.test_real_time_performance,
self.test_safety_systems
]
def run_all_tests(self) -> Dict[str, Any]:
"""Run all integration tests"""
print("Starting AI-Robot Brain Integration Tests...")
for test_func in self.integration_tests:
test_name = test_func.__name__
print(f"Running {test_name}...")
start_time = time.time()
test_result = test_func()
end_time = time.time()
self.test_results[test_name] = {
'result': test_result,
'execution_time': end_time - start_time,
'status': 'passed' if test_result else 'failed'
}
# Run performance benchmarks
self.run_performance_benchmarks()
# Generate comprehensive report
report = self.generate_test_report()
return {
'test_results': self.test_results,
'performance_benchmarks': self.performance_benchmarks,
'overall_report': report,
'all_passed': all(result['status'] == 'passed' for result in self.test_results.values())
}
def test_perception_integration(self) -> bool:
"""Test perception module integration"""
try:
# Create perception task
task = CognitiveTask(
id='test_perception',
module_type=CognitiveModuleType.PERCEPTION,
priority=8,
deadline=time.time() + 5.0,
data={
'camera': {'image': np.random.rand(480, 640, 3)},
'depth': {'data': np.random.rand(480, 640)},
'imu': {'acceleration': [0, 0, 9.81], 'orientation': [1, 0, 0, 0]}
}
)
# Execute task
result = self.brain.execute_task(task)
# Validate result
required_keys = ['fused_perception', 'detected_objects', 'scene_description']
return all(key in result for key in required_keys)
except Exception as e:
print(f"Perception integration test failed: {e}")
return False
def test_planning_integration(self) -> bool:
"""Test planning module integration"""
try:
# Create planning task
task = CognitiveTask(
id='test_planning',
module_type=CognitiveModuleType.PLANNING,
priority=7,
deadline=time.time() + 5.0,
data={
'goal': {'type': 'navigate', 'location': [1.0, 2.0, 0.0]},
'current_state': {'position': [0, 0, 0], 'orientation': [1, 0, 0, 0]},
'constraints': {'collision_free': True, 'max_time': 10.0}
}
)
# Execute task
result = self.brain.execute_task(task)
# Validate result
required_keys = ['high_level_plan', 'navigation_plan', 'plan_validity']
return all(key in result for key in required_keys)
except Exception as e:
print(f"Planning integration test failed: {e}")
return False
def test_control_integration(self) -> bool:
"""Test control module integration"""
try:
# Create control task
task = CognitiveTask(
id='test_control',
module_type=CognitiveModuleType.CONTROL,
priority=10, # Highest priority for safety
deadline=time.time() + 0.01, # 10ms deadline
data={
'plan': {'walking_pattern': {'steps': [], 'timing': []}},
'current_state': {
'joint_positions': [0.0] * 28, # Humanoid joints
'com_position': [0, 0, 0.8],
'stability_margin': 0.1
},
'feedback': {
'joint_angles': [0.0] * 28,
'imu_data': [0, 0, 9.81, 1, 0, 0, 0]
}
}
)
# Execute task
result = self.brain.execute_task(task)
# Validate result
required_keys = ['motor_commands', 'stability', 'safety_critical']
return all(key in result for key in required_keys)
except Exception as e:
print(f"Control integration test failed: {e}")
return False
def test_learning_integration(self) -> bool:
"""Test learning module integration"""
try:
# Create learning task
task = CognitiveTask(
id='test_learning',
module_type=CognitiveModuleType.LEARNING,
priority=5,
deadline=time.time() + 10.0,
data={
'task_type': 'reinforcement',
'experience_data': {
'state': [0.1, 0.2, 0.3],
'action': [0.4, 0.5],
'next_state': [0.15, 0.25, 0.35]
},
'performance_feedback': {
'reward': 1.0,
'success': True,
'error': 0.05
}
}
)
# Execute task
result = self.brain.execute_task(task)
# Validate result
required_keys = ['learning_results', 'performance_analysis', 'improvement_rate']
return all(key in result for key in required_keys)
except Exception as e:
print(f"Learning integration test failed: {e}")
return False
def test_multi_module_coordination(self) -> bool:
"""Test coordination between multiple modules"""
try:
# Schedule coordinated tasks
tasks = [
CognitiveTask(
id='coord_perception',
module_type=CognitiveModuleType.PERCEPTION,
priority=8,
deadline=time.time() + 2.0,
data={'camera': {'image': np.random.rand(480, 640, 3)}}
),
CognitiveTask(
id='coord_planning',
module_type=CognitiveModuleType.PLANNING,
priority=7,
deadline=time.time() + 3.0,
data={'goal': {'type': 'navigate', 'location': [1.0, 0, 0]}}
),
CognitiveTask(
id='coord_control',
module_type=CognitiveModuleType.CONTROL,
priority=9,
deadline=time.time() + 0.02, # 20ms
data={'plan': {}, 'current_state': {}, 'feedback': {}}
)
]
# Execute tasks and check coordination
results = []
for task in tasks:
result = self.brain.execute_task(task)
results.append(result)
# Check if results are properly coordinated
# This would involve checking dependencies and data flow between modules
return len(results) == len(tasks) and all(results)
except Exception as e:
print(f"Multi-module coordination test failed: {e}")
return False
def test_real_time_performance(self) -> bool:
"""Test real-time performance requirements"""
try:
# Test timing constraints
start_time = time.time()
# Schedule high-frequency control tasks
for i in range(100): # 100ms of high-frequency tasks
task = CognitiveTask(
id=f'rt_test_{i}',
module_type=CognitiveModuleType.CONTROL,
priority=10,
deadline=time.time() + 0.001, # 1ms deadline
data={'plan': {}, 'current_state': {}, 'feedback': {}}
)
result = self.brain.execute_task(task)
if time.time() > task.deadline:
return False # Missed deadline
execution_time = time.time() - start_time
return execution_time <= 0.15 # Should complete in less than 150ms
except Exception as e:
print(f"Real-time performance test failed: {e}")
return False
def test_safety_systems(self) -> bool:
"""Test safety system functionality"""
try:
# Test emergency stop scenario
emergency_task = CognitiveTask(
id='emergency_stop',
module_type=CognitiveModuleType.CONTROL,
priority=10,
deadline=time.time() + 0.005, # 5ms for emergency
data={
'plan': {},
'current_state': {
'stability_margin': -0.1, # Unstable
'safety_critical': True
},
'feedback': {}
}
)
result = self.brain.execute_task(emergency_task)
# Safety system should take priority and ensure safe state
return (result.get('safety_critical', False) and
result.get('stability', 0) > 0.5) # Improved stability
except Exception as e:
print(f"Safety systems test failed: {e}")
return False
def run_performance_benchmarks(self):
"""Run performance benchmarks"""
# Benchmark perception speed
self.performance_benchmarks['perception_speed'] = self.benchmark_perception()
# Benchmark planning speed
self.performance_benchmarks['planning_speed'] = self.benchmark_planning()
# Benchmark control loop frequency
self.performance_benchmarks['control_frequency'] = self.benchmark_control_frequency()
# Benchmark learning throughput
self.performance_benchmarks['learning_throughput'] = self.benchmark_learning()
def benchmark_perception(self) -> Dict[str, float]:
"""Benchmark perception module performance"""
start_time = time.time()
for _ in range(100):
task = CognitiveTask(
id=f'bench_perception_{_}',
module_type=CognitiveModuleType.PERCEPTION,
priority=5,
deadline=time.time() + 1.0,
data={'camera': {'image': np.random.rand(480, 640, 3)}}
)
self.brain.execute_task(task)
total_time = time.time() - start_time
avg_time = total_time / 100
return {
'average_time': avg_time,
'frames_per_second': 1.0 / avg_time if avg_time > 0 else float('inf'),
'total_time': total_time
}
def benchmark_planning(self) -> Dict[str, float]:
"""Benchmark planning module performance"""
start_time = time.time()
for _ in range(50):
task = CognitiveTask(
id=f'bench_planning_{_}',
module_type=CognitiveModuleType.PLANNING,
priority=5,
deadline=time.time() + 2.0,
data={
'goal': {'type': 'navigate', 'location': [float(_), 0, 0]},
'current_state': {'position': [0, 0, 0]},
'constraints': {}
}
)
self.brain.execute_task(task)
total_time = time.time() - start_time
avg_time = total_time / 50
return {
'average_time': avg_time,
'plans_per_second': 1.0 / avg_time if avg_time > 0 else float('inf'),
'total_time': total_time
}
def benchmark_control_frequency(self) -> Dict[str, float]:
"""Benchmark control loop frequency"""
start_time = time.time()
executed_tasks = 0
deadline = start_time + 1.0 # 1 second benchmark
while time.time() < deadline:
task = CognitiveTask(
id=f'bench_control_{executed_tasks}',
module_type=CognitiveModuleType.CONTROL,
priority=8,
deadline=time.time() + 0.001, # 1ms deadline
data={'plan': {}, 'current_state': {}, 'feedback': {}}
)
try:
self.brain.execute_task(task)
executed_tasks += 1
except:
break # Stop if execution fails
total_time = time.time() - start_time
return {
'tasks_executed': executed_tasks,
'frequency': executed_tasks / total_time if total_time > 0 else 0,
'average_time': total_time / executed_tasks if executed_tasks > 0 else float('inf')
}
def benchmark_learning(self) -> Dict[str, float]:
"""Benchmark learning module throughput"""
start_time = time.time()
for _ in range(20):
task = CognitiveTask(
id=f'bench_learning_{_}',
module_type=CognitiveModuleType.LEARNING,
priority=4,
deadline=time.time() + 5.0,
data={
'task_type': 'supervised',
'experience_data': {
'demonstration': {
'inputs': [np.random.rand(10).tolist()],
'outputs': [np.random.rand(4).tolist()]
}
}
}
)
self.brain.execute_task(task)
total_time = time.time() - start_time
avg_time = total_time / 20
return {
'average_time': avg_time,
'samples_per_second': 1.0 / avg_time if avg_time > 0 else float('inf'),
'total_time': total_time
}
def generate_test_report(self) -> str:
"""Generate comprehensive test report"""
report = "AI-ROBOT BRAIN INTEGRATION TEST REPORT\n"
report += "=" * 60 + "\n\n"
report += "TEST RESULTS:\n"
for test_name, result in self.test_results.items():
status = "✓ PASS" if result['status'] == 'passed' else "✗ FAIL"
report += f" {test_name}: {status} ({result['execution_time']:.3f}s)\n"
report += f"\nPERFORMANCE BENCHMARKS:\n"
for benchmark_name, metrics in self.performance_benchmarks.items():
report += f" {benchmark_name}:\n"
for metric, value in metrics.items():
report += f" {metric}: {value}\n"
all_passed = all(result['status'] == 'passed' for result in self.test_results.values())
report += f"\nOVERALL STATUS: {'✓ ALL TESTS PASSED' if all_passed else '✗ SOME TESTS FAILED'}\n"
return report
class SystemValidator:
"""Validate the complete AI-Robot Brain system"""
def __init__(self, brain_core: AIBrainCore):
self.brain = brain_core
self.integration_suite = AIIntegrationTestSuite(brain_core)
def validate_system(self) -> Dict[str, Any]:
"""Perform complete system validation"""
# Run integration tests
integration_results = self.integration_suite.run_all_tests()
# Check system stability
stability_metrics = self.check_system_stability()
# Check resource utilization
resource_metrics = self.check_resource_utilization()
# Check safety systems
safety_metrics = self.check_safety_systems()
# Generate validation report
validation_report = self.generate_validation_report(
integration_results, stability_metrics,
resource_metrics, safety_metrics
)
return {
'integration_results': integration_results,
'stability_metrics': stability_metrics,
'resource_metrics': resource_metrics,
'safety_metrics': safety_metrics,
'validation_report': validation_report,
'system_ready': self.is_system_ready(integration_results, stability_metrics)
}
def check_system_stability(self) -> Dict[str, float]:
"""Check system stability metrics"""
# Monitor cognitive state stability
stability_samples = []
for _ in range(100):
# Simulate system operation and monitor state changes
time.sleep(0.01) # 10ms sampling
# In a real system, this would monitor actual state changes
stability_samples.append(0.95) # Placeholder stability value
return {
'average_stability': sum(stability_samples) / len(stability_samples),
'stability_variance': np.var(stability_samples),
'minimum_stability': min(stability_samples),
'stability_samples': len(stability_samples)
}
def check_resource_utilization(self) -> Dict[str, float]:
"""Check system resource utilization"""
# This would interface with system monitoring tools
return {
'cpu_utilization': 0.65, # 65% CPU usage
'gpu_utilization': 0.75, # 75% GPU usage
'memory_utilization': 0.55, # 55% memory usage
'bandwidth_utilization': 0.30 # 30% bandwidth usage
}
def check_safety_systems(self) -> Dict[str, bool]:
"""Check safety system functionality"""
# Test various safety scenarios
safety_tests = {
'emergency_stop': self.test_emergency_stop(),
'collision_avoidance': self.test_collision_avoidance(),
'balance_recovery': self.test_balance_recovery(),
'safe_limits': self.test_safe_limits()
}
return safety_tests
def test_emergency_stop(self) -> bool:
"""Test emergency stop functionality"""
return True # Placeholder
def test_collision_avoidance(self) -> bool:
"""Test collision avoidance"""
return True # Placeholder
def test_balance_recovery(self) -> bool:
"""Test balance recovery"""
return True # Placeholder
def test_safe_limits(self) -> bool:
"""Test safe operational limits"""
return True # Placeholder
def generate_validation_report(self, integration: Dict, stability: Dict,
resources: Dict, safety: Dict) -> str:
"""Generate comprehensive validation report"""
report = "AI-ROBOT BRAIN SYSTEM VALIDATION REPORT\n"
report += "=" * 60 + "\n\n"
report += "INTEGRATION TESTS:\n"
report += integration['overall_report']
report += f"\nSTABILITY METRICS:\n"
for metric, value in stability.items():
report += f" {metric}: {value}\n"
report += f"\nRESOURCE UTILIZATION:\n"
for metric, value in resources.items():
report += f" {metric}: {value:.2%}\n"
report += f"\nSAFETY SYSTEMS:\n"
for system, status in safety.items():
report += f" {system}: {'✓ Active' if status else '✗ Inactive'}\n"
return report
def is_system_ready(self, integration_results: Dict, stability_metrics: Dict) -> bool:
"""Check if system is ready for deployment"""
# All integration tests must pass
all_tests_passed = integration_results.get('all_passed', False)
# Stability must be above threshold
avg_stability = stability_metrics.get('average_stability', 0.0)
stability_ok = avg_stability > 0.9 # 90% stability threshold
# Safety systems must be active
# This would check the actual safety system status
return all_tests_passed and stability_ok
def run_comprehensive_validation():
"""Run comprehensive validation of the complete AI-Robot Brain"""
print("Initializing AI-Robot Brain for comprehensive validation...")
# Create brain core
brain = AIBrainCore()
# Register all modules
brain.register_module(CognitiveModuleType.PERCEPTION, PerceptionModule())
brain.register_module(CognitiveModuleType.PLANNING, PlanningModule())
brain.register_module(CognitiveModuleType.CONTROL, ControlModule())
brain.register_module(CognitiveModuleType.LEARNING, LearningModule())
# Start the brain
brain.start()
try:
# Create validator
validator = SystemValidator(brain)
# Run validation
validation_results = validator.validate_system()
# Print validation report
print("\n" + validation_results['validation_report'])
# Print summary
print(f"\nSYSTEM READINESS: {'✓ READY FOR DEPLOYMENT' if validation_results['system_ready'] else '✗ NOT READY FOR DEPLOYMENT'}")
return validation_results
finally:
# Stop the brain
brain.stop()
# Example usage of the complete AI-Robot Brain
def example_usage():
"""Example of how to use the complete AI-Robot Brain system"""
print("Creating AI-Robot Brain instance...")
# Initialize the brain core
brain = AIBrainCore()
# Register cognitive modules
brain.register_module(CognitiveModuleType.PERCEPTION, PerceptionModule())
brain.register_module(CognitiveModuleType.PLANNING, PlanningModule())
brain.register_module(CognitiveModuleType.CONTROL, ControlModule())
brain.register_module(CognitiveModuleType.LEARNING, LearningModule())
# Start the cognitive system
brain.start()
try:
# Example: Process a navigation task
navigation_task = CognitiveTask(
id='navigate_to_kitchen',
module_type=CognitiveModuleType.PLANNING,
priority=7,
deadline=time.time() + 10.0,
data={
'goal': {'type': 'navigate', 'location': [5.0, 3.0, 0.0]},
'current_state': {'position': [0.0, 0.0, 0.0], 'orientation': [1, 0, 0, 0]},
'constraints': {'collision_free': True, 'prefer_shortest_path': True}
}
)
print("Scheduling navigation task...")
brain.schedule_task(navigation_task)
# Example: Process perception data
perception_task = CognitiveTask(
id='analyze_environment',
module_type=CognitiveModuleType.PERCEPTION,
priority=8,
deadline=time.time() + 2.0,
data={
'camera': {'image': np.random.rand(480, 640, 3)}, # Simulated camera data
'depth': {'data': np.random.rand(480, 640)}, # Simulated depth data
'imu': {'acceleration': [0, 0, 9.81], 'orientation': [1, 0, 0, 0]}
}
)
print("Scheduling perception task...")
brain.schedule_task(perception_task)
# Let the system run for a while to process tasks
time.sleep(5.0)
print("Cognitive tasks processed. Current state:")
print(f" Timestamp: {brain.cognitive_state.timestamp}")
print(f" Perception state keys: {list(brain.cognitive_state.perception_state.keys())}")
print(f" Planning state keys: {list(brain.cognitive_state.planning_state.keys())}")
print(f" Control state keys: {list(brain.cognitive_state.control_state.keys())}")
finally:
# Stop the brain
brain.stop()
print("AI-Robot Brain stopped.")
if __name__ == "__main__":
print("AI-Robot Brain Architecture - Complete Implementation")
print("=" * 60)
# Run example usage
example_usage()
print("\n" + "=" * 60)
print("Running comprehensive validation...")
validation_results = run_comprehensive_validation()
Summary
The AI-Robot Brain architecture presented in this chapter provides a comprehensive cognitive framework for humanoid robots, integrating perception, planning, control, and learning systems into a unified architecture. Key components include:
Core Architecture:
- Modular Design: Clear separation of cognitive modules with well-defined interfaces
- Real-time Scheduling: Priority-based task management with deadline guarantees
- Attention Mechanisms: Dynamic resource allocation based on task importance
- Memory Systems: Episodic and semantic memory for experience storage and retrieval
Cognitive Modules:
- Perception: Multi-modal sensing with sensor fusion for environmental understanding
- Planning: Hierarchical task decomposition with motion and navigation planning
- Control: Real-time motor control with safety and balance management
- Learning: Continuous adaptation through supervised, reinforcement, and transfer learning
Hardware Optimization:
- NVIDIA GPU Acceleration: Tensor Core optimization and mixed precision training
- TensorRT Integration: Model optimization for inference acceleration
- CUDA Graphs: Low-latency execution for real-time applications
Validation and Safety:
- Comprehensive Testing: Integration tests for all system components
- Performance Benchmarks: Throughput and latency measurements
- Safety Systems: Emergency stop and collision avoidance mechanisms
This architecture enables humanoid robots to perform complex tasks in dynamic environments while maintaining real-time performance and safety guarantees. The modular design allows for easy extension and adaptation to specific robot platforms and applications.
Next Steps
With the completion of Module 3 (NVIDIA Isaac SDK and Isaac Sim), we've covered the AI-Robot Brain components essential for humanoid robotics. The next module will focus on Vision-Language-Action (VLA) models and their integration with the cognitive architecture developed here, creating truly intelligent and interactive humanoid robots.
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