Advanced VLA: Social Interaction and Cognitive Integration
Welcome to Chapter 3 of Module 4: The Interactive AI Brain! This final chapter explores the cutting-edge applications of Vision-Language-Action (VLA) models in humanoid robotics, focusing on social interaction capabilities, multi-human scenarios, and the integration of VLA systems with cognitive architectures. We'll delve into how VLA models can enable truly intelligent, socially-aware humanoid robots that can interact naturally with multiple humans in complex environments.
Learning Objectives
By the end of this chapter, you will be able to:
- Implement social interaction capabilities in VLA models for humanoid robots
- Design VLA systems that can handle multi-human scenarios and social dynamics
- Integrate VLA models with cognitive architectures for higher-level reasoning
- Implement attention mechanisms for social scene understanding
- Create models that understand and respond to social cues and norms
- Design VLA systems for collaborative human-robot interaction
- Implement memory and learning mechanisms for social context
- Evaluate and validate social VLA systems
Social Interaction Capabilities
Understanding Social Context and Cues
Humanoid robots operating in human environments must understand social context, interpret social cues, and respond appropriately. This requires extending traditional VLA models to incorporate social understanding.
import torch
import torch.nn as nn
import numpy as np
from typing import Dict, List, Tuple, Optional
from dataclasses import dataclass
import cv2
from collections import defaultdict
@dataclass
class SocialInteractionData:
"""Data structure for social interaction VLA samples"""
# Visual data with social context
scene_image: torch.Tensor # Main scene camera
face_images: List[torch.Tensor] # Images of detected faces
body_poses: List[torch.Tensor] # Body pose information
gaze_directions: List[torch.Tensor] # Gaze direction for each person
# Language data with social context
spoken_command: str
speaker_id: str
social_context: str # Context like "group_conversation", "one_on_one", etc.
attention_target: str # Who the command is directed to
# Social action data
social_action: str # Social behavior to execute
interaction_type: str # "greeting", "assistance", "collaboration", etc.
personal_space_violations: List[bool] # For each detected person
# Environmental context
room_layout: Dict[str, any]
object_positions: Dict[str, List[float]]
social_norms: List[str] # Applicable social norms for the scene
class SocialAttentionMechanism(nn.Module):
"""Attention mechanism for social scene understanding"""
def __init__(self, hidden_dim: int = 512, num_heads: int = 8):
super().__init__()
self.hidden_dim = hidden_dim
self.num_heads = num_heads
self.head_dim = hidden_dim // num_heads
# Multi-head attention for social elements
self.social_query = nn.Linear(hidden_dim, hidden_dim)
self.social_key = nn.Linear(hidden_dim, hidden_dim)
self.social_value = nn.Linear(hidden_dim, hidden_dim)
# Social context encoder
self.social_context_encoder = nn.TransformerEncoder(
nn.TransformerEncoderLayer(
d_model=hidden_dim,
nhead=num_heads,
dim_feedforward=hidden_dim * 4,
batch_first=True
),
num_layers=3
)
# Person-specific attention weights
self.person_attention = nn.Linear(hidden_dim, 1)
# Social relationship encoder
self.relationship_encoder = nn.Sequential(
nn.Linear(hidden_dim * 2, hidden_dim), # Combined features of two people
nn.ReLU(),
nn.Linear(hidden_dim, hidden_dim)
)
def forward(self,
visual_features: torch.Tensor,
person_features: torch.Tensor,
social_context: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Forward pass through social attention mechanism
Args:
visual_features: (batch, seq_len, hidden_dim) - Scene features
person_features: (batch, num_people, hidden_dim) - Features for each person
social_context: (batch, hidden_dim) - Overall social context
Returns:
attended_features: (batch, hidden_dim) - Socially attended features
attention_weights: (batch, num_people) - Attention weights for each person
"""
batch_size, num_people, _ = person_features.shape
# Compute attention for each person
social_queries = self.social_query(person_features) # (batch, num_people, hidden_dim)
social_keys = self.social_key(visual_features) # (batch, seq_len, hidden_dim)
social_values = self.social_value(visual_features) # (batch, seq_len, hidden_dim)
# Multi-head attention
attention_scores = torch.bmm(social_queries, social_keys.transpose(1, 2)) # (batch, num_people, seq_len)
attention_weights = torch.softmax(attention_scores / np.sqrt(self.head_dim), dim=-1)
# Apply attention to get person-specific attended features
attended_person_features = torch.bmm(attention_weights, social_values) # (batch, num_people, hidden_dim)
# Compute person importance weights
person_importance = self.person_attention(attended_person_features).squeeze(-1) # (batch, num_people)
person_attention_weights = torch.softmax(person_importance, dim=-1) # (batch, num_people)
# Weighted sum of person features based on social importance
attended_features = torch.bmm(
person_attention_weights.unsqueeze(1),
attended_person_features
).squeeze(1) # (batch, hidden_dim)
# Integrate with social context
combined_context = torch.cat([attended_features, social_context], dim=-1)
final_features = self.relationship_encoder(combined_context)
return final_features, person_attention_weights
class SocialSceneUnderstanding(nn.Module):
"""Module for understanding social scenes and relationships"""
def __init__(self, hidden_dim: int = 512):
super().__init__()
self.hidden_dim = hidden_dim
# Person detection and feature extraction
self.person_detector = PersonDetector(hidden_dim)
# Social relationship classifier
self.relationship_classifier = nn.Sequential(
nn.Linear(hidden_dim * 2, hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, 64), # 64 different relationship types
nn.Softmax(dim=-1)
)
# Social norm encoder
self.social_norm_encoder = nn.Sequential(
nn.Linear(128, hidden_dim), # 128 social norm features
nn.ReLU(),
nn.Linear(hidden_dim, hidden_dim)
)
# Group activity detector
self.group_activity_detector = nn.Sequential(
nn.Linear(hidden_dim * 4, hidden_dim), # Combined features of 4 people max
nn.ReLU(),
nn.Linear(hidden_dim, 32), # 32 different group activities
nn.Softmax(dim=-1)
)
def forward(self,
scene_image: torch.Tensor,
social_norms: List[str]) -> Dict[str, torch.Tensor]:
"""Analyze social scene and extract social understanding features"""
# Detect people in the scene
person_detections = self.person_detector(scene_image)
num_people = len(person_detections['person_features'])
# Classify relationships between people
relationship_features = []
for i in range(num_people):
for j in range(i + 1, num_people):
combined_features = torch.cat([
person_detections['person_features'][i],
person_detections['person_features'][j]
], dim=-1)
relationship = self.relationship_classifier(combined_features)
relationship_features.append(relationship)
# Encode social norms
norm_features = self._encode_social_norms(social_norms)
# Detect group activities
if num_people >= 2:
group_features = torch.cat(person_detections['person_features'][:4], dim=-1) # Max 4 people
group_activity = self.group_activity_detector(group_features)
else:
group_activity = torch.zeros(32) # No group activity
return {
'person_detections': person_detections,
'relationship_features': relationship_features,
'social_norm_features': norm_features,
'group_activity': group_activity,
'num_people': num_people
}
def _encode_social_norms(self, norms: List[str]) -> torch.Tensor:
"""Encode social norms into feature vector"""
# This would typically use a more sophisticated encoding
# For now, use a simple embedding approach
norm_vector = torch.zeros(128) # 128-dimensional norm vector
# Simple encoding based on norm categories
norm_categories = {
'personal_space': 0,
'eye_contact': 20,
'turn_taking': 40,
'respect': 60,
'cooperation': 80,
'etiquette': 100
}
for norm in norms:
for category, offset in norm_categories.items():
if category in norm.lower():
norm_vector[offset:offset+5] = 1.0
break
return norm_vector
class PersonDetector(nn.Module):
"""Detect and extract features for people in scene"""
def __init__(self, hidden_dim: int = 512):
super().__init__()
self.hidden_dim = hidden_dim
# Visual feature extraction for people
self.visual_encoder = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3),
nn.BatchNorm2d(64),
nn.ReLU(),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
nn.Conv2d(64, 128, kernel_size=3, padding=1),
nn.BatchNorm2d(128),
nn.ReLU(),
nn.AdaptiveAvgPool2d((1, 1)),
nn.Flatten(),
nn.Linear(128, hidden_dim),
nn.ReLU()
)
# Face feature extractor
self.face_extractor = nn.Sequential(
nn.Linear(512, hidden_dim), # Pre-extracted face features
nn.ReLU()
)
# Body pose feature extractor
self.pose_extractor = nn.Sequential(
nn.Linear(34, hidden_dim), # 17 keypoints * 2 (x, y)
nn.ReLU()
)
# Gaze direction encoder
self.gaze_encoder = nn.Sequential(
nn.Linear(3, hidden_dim), # 3D gaze vector
nn.ReLU()
)
def forward(self, scene_image: torch.Tensor) -> Dict:
"""Detect people and extract their features"""
batch_size = scene_image.size(0)
# Extract visual features for the scene
visual_features = self.visual_encoder(scene_image)
# For this example, we'll simulate person detection
# In practice, this would use object detection and pose estimation
num_simulated_people = 2 # Simulate 2 people in scene
person_features = []
face_features = []
pose_features = []
gaze_features = []
for i in range(num_simulated_people):
# Simulate person features
person_feat = torch.randn(batch_size, self.hidden_dim) * 0.1 + visual_features
face_feat = torch.randn(batch_size, self.hidden_dim) * 0.1
pose_feat = torch.randn(batch_size, self.hidden_dim) * 0.1
gaze_feat = torch.randn(batch_size, self.hidden_dim) * 0.1
person_features.append(person_feat)
face_features.append(face_feat)
pose_features.append(pose_feat)
gaze_features.append(gaze_feat)
return {
'person_features': torch.stack(person_features, dim=1) if person_features else torch.empty(batch_size, 0, self.hidden_dim),
'face_features': torch.stack(face_features, dim=1) if face_features else torch.empty(batch_size, 0, self.hidden_dim),
'pose_features': torch.stack(pose_features, dim=1) if pose_features else torch.empty(batch_size, 0, self.hidden_dim),
'gaze_features': torch.stack(gaze_features, dim=1) if gaze_features else torch.empty(batch_size, 0, self.hidden_dim),
'num_people': num_simulated_people
}
Social Interaction VLA Model
Now let's create a specialized VLA model that incorporates social understanding:
class SocialVLA(nn.Module):
"""Vision-Language-Action model with social interaction capabilities"""
def __init__(self,
num_cameras: int = 3,
vocab_size: int = 10000,
hidden_dim: int = 512,
action_dim: int = 28,
social_features_dim: int = 256):
super().__init__()
# Standard VLA components
self.vision_encoder = MultiViewVisionEncoder(
num_cameras=num_cameras,
hidden_dim=hidden_dim
)
self.language_encoder = nn.TransformerEncoder(
nn.TransformerEncoderLayer(
d_model=hidden_dim,
nhead=8,
dim_feedforward=hidden_dim * 4,
batch_first=True
),
num_layers=6
)
self.text_embedding = nn.Embedding(vocab_size, hidden_dim)
# Social understanding components
self.social_scene_understanding = SocialSceneUnderstanding(hidden_dim)
self.social_attention = SocialAttentionMechanism(hidden_dim)
# Social context integration
self.social_context_integrator = nn.Sequential(
nn.Linear(hidden_dim + social_features_dim, hidden_dim * 2),
nn.ReLU(),
nn.Linear(hidden_dim * 2, hidden_dim),
nn.LayerNorm(hidden_dim)
)
# Social action decoder
self.social_action_decoder = nn.Sequential(
nn.Linear(hidden_dim, hidden_dim * 2),
nn.ReLU(),
nn.Dropout(0.1),
nn.Linear(hidden_dim * 2, hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, action_dim + 4), # +4 for social actions (gaze, gesture, etc.)
nn.Tanh()
)
# Social norm compliance layer
self.social_norm_compliance = SocialNormComplianceLayer(hidden_dim)
self.dropout = nn.Dropout(0.1)
def forward(self,
multi_view_images: torch.Tensor,
text_tokens: torch.Tensor,
proprio_state: torch.Tensor,
social_context: Dict[str, any]) -> Dict[str, torch.Tensor]:
"""
Forward pass with social interaction capabilities
Args:
multi_view_images: Multi-view camera images
text_tokens: Language command tokens
proprio_state: Current robot state
social_context: Dictionary containing social information
Returns:
Dictionary with actions and social outputs
"""
batch_size = multi_view_images.size(0)
# Standard VLA processing
vision_features = self.vision_encoder(multi_view_images)
text_embedded = self.text_embedding(text_tokens)
text_encoded = self.language_encoder(text_embedded)
text_features = text_encoded.mean(dim=1)
# Social scene understanding
social_analysis = self.social_scene_understanding(
multi_view_images[:, 0], # Use first camera for scene understanding
social_context.get('social_norms', [])
)
# Social attention mechanism
if social_analysis['num_people'] > 0:
person_features = social_analysis['person_detections']['person_features']
social_attention_features, person_weights = self.social_attention(
vision_features.unsqueeze(1), # Add sequence dimension
person_features,
social_analysis['social_norm_features'].unsqueeze(1).expand(-1, person_features.size(1), -1)
)
else:
social_attention_features = torch.zeros(batch_size, vision_features.size(-1))
person_weights = torch.zeros(batch_size, 1)
# Integrate social context
combined_features = torch.cat([
vision_features,
text_features,
proprio_state,
social_attention_features
], dim=-1)
integrated_features = self.social_context_integrator(combined_features)
integrated_features = self.dropout(integrated_features)
# Generate social actions
social_actions = self.social_action_decoder(integrated_features)
# Apply social norm compliance
compliant_actions = self.social_norm_compliance(
social_actions,
social_analysis,
social_context
)
# Split into robot actions and social actions
robot_actions = compliant_actions[:, :28] # First 28 for robot joints
social_signals = compliant_actions[:, 28:] # Remaining for social signals
return {
'robot_actions': robot_actions,
'social_signals': social_signals,
'person_attention_weights': person_weights,
'social_analysis': social_analysis,
'compliance_adjustments': compliant_actions - social_actions # Show adjustments made
}
class SocialNormComplianceLayer(nn.Module):
"""Layer to ensure actions comply with social norms"""
def __init__(self, hidden_dim: int = 512):
super().__init__()
self.norm_adjustment_network = nn.Sequential(
nn.Linear(hidden_dim + 128, hidden_dim), # +128 for norm features
nn.ReLU(),
nn.Linear(hidden_dim, hidden_dim),
nn.Sigmoid() # Adjustment factor between 0 and 1
)
def forward(self,
actions: torch.Tensor,
social_analysis: Dict,
social_context: Dict) -> torch.Tensor:
"""Adjust actions to comply with social norms"""
# Get social norm features
norm_features = social_analysis.get('social_norm_features', torch.zeros(128).expand(actions.size(0), -1))
# Combine with action features
combined = torch.cat([actions, norm_features], dim=-1)
# Calculate adjustment factors
adjustment_factors = self.norm_adjustment_network(combined)
# Apply adjustments to ensure social compliance
# This is a simplified example - real implementation would be more nuanced
adjusted_actions = actions * adjustment_factors
# Ensure certain social constraints
adjusted_actions = self._apply_social_constraints(adjusted_actions, social_analysis)
return adjusted_actions
def _apply_social_constraints(self, actions: torch.Tensor, social_analysis: Dict) -> torch.Tensor:
"""Apply hard social constraints"""
constrained_actions = actions.clone()
# Example: Maintain personal space (simplified)
# In real implementation, this would check distances to people
if social_analysis.get('num_people', 0) > 0:
# Reduce aggressive movement if people are nearby
movement_magnitude = torch.norm(actions[:, :6], dim=1, keepdim=True) # First 6 joints for movement
safe_movement = torch.clamp(movement_magnitude, max=0.8) # Limit movement
scale_factor = safe_movement / (movement_magnitude + 1e-6)
constrained_actions[:, :6] = actions[:, :6] * scale_factor
return constrained_actions
def example_social_vla():
"""Example usage of Social VLA model"""
# Create social VLA model
model = SocialVLA()
# Create sample inputs
batch_size = 1
multi_view_images = torch.randn(batch_size, 3, 3, 224, 224)
text_tokens = torch.randint(0, 10000, (batch_size, 10))
proprio_state = torch.randn(batch_size, 28 * 2)
# Social context
social_context = {
'social_norms': ['maintain_personal_space', 'make_eye_contact', 'respect_turn_taking'],
'room_layout': {'table': [1.0, 0.0, 0.0], 'chairs': [[0.5, 0.5, 0.0], [-0.5, 0.5, 0.0]]},
'object_positions': {'cup': [1.0, 0.5, 0.8]},
'detected_people': 2
}
# Forward pass
outputs = model(multi_view_images, text_tokens, proprio_state, social_context)
print("Social VLA Outputs:")
print(f"Robot actions shape: {outputs['robot_actions'].shape}")
print(f"Social signals shape: {outputs['social_signals'].shape}")
print(f"Person attention weights shape: {outputs['person_attention_weights'].shape}")
print(f"Number of people detected: {outputs['social_analysis']['num_people']}")
print(f"Compliance adjustments applied: {not torch.allclose(outputs['compliance_adjustments'], torch.zeros_like(outputs['compliance_adjustments']))}")
example_social_vla()
Multi-Human Scenarios and Social Dynamics
Handling Multiple Humans
Humanoid robots often need to interact with multiple humans simultaneously, requiring sophisticated social reasoning and attention mechanisms.
class MultiHumanInteractionManager:
"""Manage interactions with multiple humans"""
def __init__(self, max_people: int = 10):
self.max_people = max_people
self.people_tracker = PeopleTracker()
self.social_scheduler = SocialScheduler()
self.attention_allocator = AttentionAllocator()
def process_multi_human_scene(self,
scene_image: torch.Tensor,
audio_input: torch.Tensor,
robot_state: Dict) -> Dict[str, any]:
"""Process scene with multiple humans and determine interaction strategy"""
# Track people in scene
tracked_people = self.people_tracker.track_people(scene_image)
# Determine interaction priorities
interaction_priorities = self._determine_interaction_priorities(
tracked_people, audio_input, robot_state
)
# Allocate attention based on priorities
attention_allocation = self.attention_allocator.allocate_attention(
tracked_people, interaction_priorities
)
# Schedule interactions
interaction_schedule = self.social_scheduler.schedule_interactions(
tracked_people, attention_allocation, robot_state
)
return {
'tracked_people': tracked_people,
'interaction_priorities': interaction_priorities,
'attention_allocation': attention_allocation,
'interaction_schedule': interaction_schedule,
'focus_person': self._select_focus_person(tracked_people, attention_allocation)
}
def _determine_interaction_priorities(self, people: List[Dict], audio: torch.Tensor, robot_state: Dict) -> List[float]:
"""Determine priority for interacting with each person"""
priorities = []
for person in people:
priority = 0.0
# Factors affecting priority:
# 1. Distance to robot
distance = person.get('distance', float('inf'))
priority += max(0, 1.0 - distance / 5.0) # Higher priority for closer people
# 2. Social salience (speaking, gesturing, etc.)
if person.get('is_speaking', False):
priority += 0.3
if person.get('is_gesturing', False):
priority += 0.2
# 3. Previous interaction history
last_interaction = person.get('last_interaction_time', 0)
time_since_interaction = time.time() - last_interaction
if time_since_interaction > 30: # 30 seconds
priority += 0.1 # Higher priority for people not interacted with recently
# 4. Social role (if known)
social_role = person.get('role', 'other')
if social_role in ['host', 'instructor', 'supervisor']:
priority += 0.4
priorities.append(min(priority, 1.0))
return priorities
def _select_focus_person(self, people: List[Dict], attention_allocation: List[float]) -> Optional[Dict]:
"""Select the person to focus on based on attention allocation"""
if not people or not attention_allocation:
return None
max_attention_idx = np.argmax(attention_allocation)
return people[max_attention_idx]
class PeopleTracker:
"""Track multiple people in the environment"""
def __init__(self):
self.tracked_ids = set()
self.person_data = {}
self.next_id = 0
def track_people(self, scene_image: torch.Tensor) -> List[Dict]:
"""Track people in the current scene"""
# This would use computer vision to detect and track people
# For simulation, we'll create synthetic data
num_detected = np.random.randint(1, 4) # 1-3 people
tracked_people = []
for i in range(num_detected):
person_id = f"person_{self.next_id}"
self.next_id += 1
person_info = {
'id': person_id,
'position': [np.random.uniform(-2, 2), np.random.uniform(-2, 2), 0], # x, y, z
'orientation': np.random.uniform(0, 2 * np.pi), # facing direction
'distance': np.random.uniform(0.5, 3.0),
'is_speaking': np.random.random() > 0.7, # 30% chance of speaking
'is_gesturing': np.random.random() > 0.8, # 20% chance of gesturing
'age_group': np.random.choice(['child', 'adult', 'elderly']),
'gender': np.random.choice(['male', 'female']),
'last_seen': time.time(),
'last_interaction_time': time.time() - np.random.uniform(0, 120) # 0-120 seconds ago
}
tracked_people.append(person_info)
self.person_data[person_id] = person_info
return tracked_people
class SocialScheduler:
"""Schedule social interactions based on context"""
def __init__(self):
self.interaction_queue = []
self.active_interactions = {}
def schedule_interactions(self, people: List[Dict], attention_allocation: List[float], robot_state: Dict) -> List[Dict]:
"""Schedule interactions with multiple people"""
scheduled_interactions = []
for i, person in enumerate(people):
attention = attention_allocation[i] if i < len(attention_allocation) else 0
if attention > 0.3: # Only schedule if attention is significant
interaction = self._create_interaction_for_person(person, attention, robot_state)
scheduled_interactions.append(interaction)
return scheduled_interactions
def _create_interaction_for_person(self, person: Dict, attention: float, robot_state: Dict) -> Dict:
"""Create specific interaction for a person"""
interaction_type = self._determine_interaction_type(person, attention)
return {
'person_id': person['id'],
'interaction_type': interaction_type,
'priority': attention,
'duration_estimate': self._estimate_interaction_duration(interaction_type),
'required_actions': self._get_required_actions(interaction_type, person),
'social_norms': self._get_applicable_social_norms(interaction_type, person)
}
def _determine_interaction_type(self, person: Dict, attention: float) -> str:
"""Determine appropriate interaction type based on context"""
if attention > 0.8:
if person.get('is_speaking', False):
return 'response_to_speech'
elif person.get('distance', float('inf')) < 1.0:
return 'close_interaction'
elif attention > 0.5:
if person.get('age_group') == 'child':
return 'child_engagement'
elif person.get('age_group') == 'elderly':
return 'elderly_assistance'
elif attention > 0.3:
return 'acknowledgment'
return 'monitoring'
def _estimate_interaction_duration(self, interaction_type: str) -> float:
"""Estimate duration of interaction"""
duration_map = {
'response_to_speech': 5.0,
'close_interaction': 10.0,
'child_engagement': 15.0,
'elderly_assistance': 20.0,
'acknowledgment': 2.0,
'monitoring': 0.5
}
return duration_map.get(interaction_type, 5.0)
def _get_required_actions(self, interaction_type: str, person: Dict) -> List[str]:
"""Get required actions for interaction"""
action_map = {
'response_to_speech': ['turn_towards', 'make_eye_contact', 'gesture_response'],
'close_interaction': ['approach', 'greet', 'wait_for_response'],
'child_engagement': ['crouch_down', 'friendly_gesture', 'simple_language'],
'elderly_assistance': ['speak_clearly', 'offer_assistance', 'respect_pace'],
'acknowledgment': ['nod', 'smile', 'verbal_acknowledgment'],
'monitoring': ['maintain_awareness', 'avoid_obstruction']
}
return action_map.get(interaction_type, [])
def _get_applicable_social_norms(self, interaction_type: str, person: Dict) -> List[str]:
"""Get social norms applicable to interaction"""
norms = ['respect_personal_space', 'maintain_eye_contact', 'use_appropriate_gestures']
if person.get('age_group') == 'child':
norms.extend(['speak_at_child_level', 'use_simple_language'])
elif person.get('age_group') == 'elderly':
norms.extend(['speak_clearly', 'allow_extra_time'])
if interaction_type in ['response_to_speech', 'close_interaction']:
norms.append('take_turns_speaking')
return norms
class AttentionAllocator:
"""Allocate attention among multiple people"""
def __init__(self):
self.attention_history = defaultdict(list)
def allocate_attention(self, people: List[Dict], priorities: List[float]) -> List[float]:
"""Allocate attention based on priorities and history"""
if not people:
return []
# Normalize priorities
if sum(priorities) == 0:
return [1.0 / len(people)] * len(people)
# Apply softmax to convert priorities to probabilities
priorities_tensor = torch.tensor(priorities, dtype=torch.float32)
attention_weights = torch.softmax(priorities_tensor / 0.5, dim=0) # Temperature parameter
# Convert to list
attention_weights = attention_weights.tolist()
# Apply temporal consistency (smooth attention changes)
attention_weights = self._smooth_attention_changes(people, attention_weights)
return attention_weights
def _smooth_attention_changes(self, people: List[Dict], new_weights: List[float]) -> List[float]:
"""Smooth attention allocation over time to avoid abrupt changes"""
if not people:
return new_weights
smoothed_weights = []
smoothing_factor = 0.3 # Higher = more smoothing
for i, person in enumerate(people):
person_id = person['id']
prev_attention = self.attention_history[person_id][-1] if self.attention_history[person_id] else 0.0
# Blend new attention with previous attention
smoothed_weight = (1 - smoothing_factor) * new_weights[i] + smoothing_factor * prev_attention
smoothed_weights.append(smoothed_weight)
# Update history
self.attention_history[person_id].append(smoothed_weight)
if len(self.attention_history[person_id]) > 10: # Keep last 10 values
self.attention_history[person_id] = self.attention_history[person_id][-10:]
# Renormalize to sum to 1
total_weight = sum(smoothed_weights)
if total_weight > 0:
smoothed_weights = [w / total_weight for w in smoothed_weights]
return smoothed_weights
class MultiHumanVLA(nn.Module):
"""VLA model specifically designed for multi-human scenarios"""
def __init__(self,
num_cameras: int = 3,
vocab_size: int = 10000,
hidden_dim: int = 512,
action_dim: int = 28,
max_people: int = 10):
super().__init__()
self.max_people = max_people
# Standard VLA components
self.vision_encoder = MultiViewVisionEncoder(num_cameras, hidden_dim)
self.language_encoder = nn.TransformerEncoder(
nn.TransformerEncoderLayer(d_model=hidden_dim, nhead=8, batch_first=True),
num_layers=6
)
self.text_embedding = nn.Embedding(vocab_size, hidden_dim)
# Multi-human specific components
self.person_feature_extractor = PersonFeatureExtractor(hidden_dim)
self.group_attention = GroupAttentionMechanism(hidden_dim)
self.social_role_encoder = SocialRoleEncoder(hidden_dim)
self.multi_human_fusion = MultiHumanFusion(hidden_dim)
# Action decoders
self.individual_action_decoder = nn.Sequential(
nn.Linear(hidden_dim, hidden_dim * 2),
nn.ReLU(),
nn.Linear(hidden_dim * 2, action_dim),
nn.Tanh()
)
self.group_action_decoder = nn.Sequential(
nn.Linear(hidden_dim, hidden_dim * 2),
nn.ReLU(),
nn.Linear(hidden_dim * 2, action_dim),
nn.Tanh()
)
self.attention_control = nn.Sequential(
nn.Linear(hidden_dim, hidden_dim // 2),
nn.ReLU(),
nn.Linear(hidden_dim // 2, max_people), # Attention weights for each person
nn.Softmax(dim=-1)
)
def forward(self,
multi_view_images: torch.Tensor,
text_tokens: torch.Tensor,
proprio_state: torch.Tensor,
people_info: List[Dict],
social_context: Dict) -> Dict[str, torch.Tensor]:
"""
Forward pass for multi-human scenario
Args:
multi_view_images: Multi-view images
text_tokens: Language input
proprio_state: Robot state
people_info: List of information about detected people
social_context: Social context information
Returns:
Dictionary with actions and social outputs
"""
batch_size = multi_view_images.size(0)
# Standard encoding
vision_features = self.vision_encoder(multi_view_images)
text_embedded = self.text_embedding(text_tokens)
text_encoded = self.language_encoder(text_embedded)
text_features = text_encoded.mean(dim=1)
# Extract features for each person
if people_info:
person_features = self.person_feature_extractor(people_info, batch_size)
num_people = min(len(people_info), self.max_people)
# Group attention mechanism
group_features, person_attention = self.group_attention(
vision_features,
person_features,
text_features,
num_people
)
# Social role encoding
role_features = self.social_role_encoder(people_info, batch_size)
# Fuse all multi-human information
multi_human_features = self.multi_human_fusion(
group_features,
role_features,
text_features,
proprio_state
)
# Generate attention weights for each person
attention_weights = self.attention_control(multi_human_features)
# Generate actions
individual_actions = self.individual_action_decoder(multi_human_features)
group_actions = self.group_action_decoder(multi_human_features)
# Combine actions based on attention
combined_actions = (individual_actions + group_actions) / 2
else:
# No people detected - generate general actions
combined_features = torch.cat([vision_features, text_features, proprio_state], dim=-1)
combined_actions = self.individual_action_decoder(combined_features)
attention_weights = torch.zeros(batch_size, self.max_people)
return {
'actions': combined_actions,
'person_attention_weights': attention_weights,
'individual_actions': individual_actions if 'individual_actions' in locals() else combined_actions,
'group_actions': group_actions if 'group_actions' in locals() else combined_actions,
'num_people_detected': len(people_info) if people_info else 0
}
class PersonFeatureExtractor(nn.Module):
"""Extract features for multiple people"""
def __init__(self, hidden_dim: int):
super().__init__()
self.hidden_dim = hidden_dim
# Encoder for individual person features
self.person_encoder = nn.Sequential(
nn.Linear(10, hidden_dim), # Simplified person features: position(3) + orientation(1) + distance(1) + other(5)
nn.ReLU(),
nn.Linear(hidden_dim, hidden_dim)
)
def forward(self, people_info: List[Dict], batch_size: int) -> torch.Tensor:
"""Extract features for all people in the scene"""
person_features = []
for person in people_info:
# Create feature vector for this person
person_vec = torch.zeros(10) # 10-dimensional feature vector
# Position features (3D)
pos = person.get('position', [0, 0, 0])
person_vec[0:3] = torch.tensor(pos, dtype=torch.float32)
# Orientation
person_vec[3] = person.get('orientation', 0.0)
# Distance
person_vec[4] = person.get('distance', 0.0)
# Social features
person_vec[5] = 1.0 if person.get('is_speaking', False) else 0.0
person_vec[6] = 1.0 if person.get('is_gesturing', False) else 0.0
person_vec[7] = 1.0 if person.get('age_group') == 'child' else 0.0
person_vec[8] = 1.0 if person.get('age_group') == 'elderly' else 0.0
person_vec[9] = 1.0 if person.get('gender') == 'female' else 0.0
# Encode
encoded = self.person_encoder(person_vec.unsqueeze(0).expand(batch_size, -1))
person_features.append(encoded)
if person_features:
# Stack features: (batch, num_people, hidden_dim)
return torch.stack(person_features, dim=1)
else:
# Return empty tensor if no people detected
return torch.zeros(batch_size, 0, self.hidden_dim)
class GroupAttentionMechanism(nn.Module):
"""Attention mechanism for group interactions"""
def __init__(self, hidden_dim: int, num_heads: int = 8):
super().__init__()
self.hidden_dim = hidden_dim
self.num_heads = num_heads
self.head_dim = hidden_dim // num_heads
# Multi-head attention components
self.query = nn.Linear(hidden_dim, hidden_dim)
self.key = nn.Linear(hidden_dim, hidden_dim)
self.value = nn.Linear(hidden_dim, hidden_dim)
# Group-specific processing
self.group_processor = nn.Sequential(
nn.Linear(hidden_dim * 2, hidden_dim), # Combined features
nn.ReLU(),
nn.Linear(hidden_dim, hidden_dim)
)
def forward(self,
scene_features: torch.Tensor,
person_features: torch.Tensor,
text_features: torch.Tensor,
num_people: int) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Apply attention to group of people
Args:
scene_features: (batch, hidden_dim) - Scene-level features
person_features: (batch, num_people, hidden_dim) - Features for each person
text_features: (batch, hidden_dim) - Text features
num_people: Number of people detected
Returns:
group_features: (batch, hidden_dim) - Group-level features
attention_weights: (batch, num_people) - Attention weights for each person
"""
if num_people == 0:
return scene_features, torch.zeros(scene_features.size(0), 1)
batch_size = scene_features.size(0)
# Create group context by combining scene and text features
group_context = (scene_features + text_features) / 2 # (batch, hidden_dim)
# Apply multi-head attention
queries = self.query(group_context).unsqueeze(1) # (batch, 1, hidden_dim)
keys = self.key(person_features) # (batch, num_people, hidden_dim)
values = self.value(person_features) # (batch, num_people, hidden_dim)
# Compute attention scores
attention_scores = torch.bmm(queries, keys.transpose(1, 2)) # (batch, 1, num_people)
attention_weights = torch.softmax(attention_scores.squeeze(1) / np.sqrt(self.head_dim), dim=-1) # (batch, num_people)
# Apply attention to get group features
attended_features = torch.bmm(attention_weights.unsqueeze(1), values).squeeze(1) # (batch, hidden_dim)
# Combine with context
group_features = self.group_processor(
torch.cat([attended_features, group_context], dim=-1)
)
return group_features, attention_weights
class SocialRoleEncoder(nn.Module):
"""Encode social roles of people in the scene"""
def __init__(self, hidden_dim: int):
super().__init__()
self.role_embedding = nn.Embedding(20, hidden_dim // 4) # 20 different roles
self.role_encoder = nn.Sequential(
nn.Linear(hidden_dim // 4 + 5, hidden_dim), # Role embedding + other features
nn.ReLU(),
nn.Linear(hidden_dim, hidden_dim)
)
def forward(self, people_info: List[Dict], batch_size: int) -> torch.Tensor:
"""Encode social roles for all people"""
role_features = []
for person in people_info:
# Role embedding (simplified)
role_str = person.get('role', 'other')
role_map = {
'other': 0, 'adult': 1, 'child': 2, 'elderly': 3, 'host': 4,
'guest': 5, 'instructor': 6, 'student': 7, 'supervisor': 8, 'worker': 9
}
role_idx = role_map.get(role_str, 0)
role_embedding = self.role_embedding(torch.tensor([role_idx]).expand(batch_size))
# Other role-related features
other_features = torch.zeros(batch_size, 5)
other_features[:, 0] = 1.0 if person.get('age_group') == 'child' else 0.0
other_features[:, 1] = 1.0 if person.get('age_group') == 'elderly' else 0.0
other_features[:, 2] = 1.0 if person.get('gender') == 'female' else 0.0
other_features[:, 3] = person.get('speaking_time', 0.0) # How long they've been speaking
other_features[:, 4] = person.get('interaction_frequency', 0.0) # How often interacted
# Combine features
combined = torch.cat([role_embedding, other_features], dim=-1)
encoded = self.role_encoder(combined)
role_features.append(encoded)
if role_features:
return torch.stack(role_features, dim=1).mean(dim=1) # Average across people
else:
return torch.zeros(batch_size, self.role_encoder[-1].out_features)
class MultiHumanFusion(nn.Module):
"""Fuse information from multiple people and context"""
def __init__(self, hidden_dim: int):
super().__init__()
self.fusion_network = nn.Sequential(
nn.Linear(hidden_dim * 3, hidden_dim * 2), # group + role + text
nn.ReLU(),
nn.Linear(hidden_dim * 2, hidden_dim),
nn.LayerNorm(hidden_dim)
)
def forward(self, group_features: torch.Tensor, role_features: torch.Tensor, text_features: torch.Tensor, proprio_state: torch.Tensor) -> torch.Tensor:
"""Fuse multi-human information"""
combined = torch.cat([
group_features,
role_features,
text_features
], dim=-1)
return self.fusion_network(combined)
def example_multi_human_vla():
"""Example usage of Multi-Human VLA"""
# Create multi-human VLA model
model = MultiHumanVLA()
# Create sample inputs
batch_size = 1
multi_view_images = torch.randn(batch_size, 3, 3, 224, 224)
text_tokens = torch.randint(0, 10000, (batch_size, 10))
proprio_state = torch.randn(batch_size, 28 * 2)
# Simulated people info
people_info = [
{
'id': 'person_1',
'position': [1.0, 0.5, 0.0],
'orientation': 1.57, # 90 degrees
'distance': 1.2,
'is_speaking': True,
'is_gesturing': False,
'age_group': 'adult',
'gender': 'male'
},
{
'id': 'person_2',
'position': [-0.5, 1.0, 0.0],
'orientation': 0.0,
'distance': 2.0,
'is_speaking': False,
'is_gesturing': True,
'age_group': 'elderly',
'gender': 'female'
}
]
social_context = {
'conversation_topic': 'greeting',
'room_type': 'living_room',
'time_of_day': 'afternoon'
}
# Forward pass
outputs = model(multi_view_images, text_tokens, proprio_state, people_info, social_context)
print("Multi-Human VLA Outputs:")
print(f"Actions shape: {outputs['actions'].shape}")
print(f"Person attention weights shape: {outputs['person_attention_weights'].shape}")
print(f"Individual actions shape: {outputs['individual_actions'].shape}")
print(f"Group actions shape: {outputs['group_actions'].shape}")
print(f"Number of people detected: {outputs['num_people_detected']}")
print(f"Attention distribution: {outputs['person_attention_weights'][0].tolist()}")
example_multi_human_vla()
Cognitive Architecture Integration
Integrating VLA with Cognitive Systems
To create truly intelligent humanoid robots, VLA models must be integrated with broader cognitive architectures that handle reasoning, memory, planning, and learning.
class CognitiveVLAIntegration:
"""Integrate VLA models with cognitive architecture"""
def __init__(self, vla_model: nn.Module):
self.vla_model = vla_model
# Cognitive components
self.memory_system = MemorySystem()
self.reasoning_engine = ReasoningEngine()
self.planning_system = PlanningSystem()
self.learning_module = LearningModule()
# Integration interfaces
self.perception_interface = PerceptionInterface()
self.action_interface = ActionInterface()
self.language_interface = LanguageInterface()
def process_cognitive_cycle(self,
sensory_input: Dict,
task_goal: str,
context: Dict) -> Dict:
"""Process one cognitive cycle integrating VLA with cognitive systems"""
# 1. Perception and understanding
perceptual_understanding = self.perception_interface.process(
sensory_input['visual'],
sensory_input['audio'],
context
)
# 2. Language understanding
language_understanding = self.language_interface.process(
sensory_input['text'],
perceptual_understanding
)
# 3. VLA processing for immediate actions
vla_output = self.vla_model(
multi_view_images=sensory_input['visual'],
text_tokens=language_understanding['tokens'],
proprio_state=sensory_input['proprioception'],
people_info=perceptual_understanding.get('people', []),
social_context=context
)
# 4. Memory integration
self.memory_system.store_episode({
'sensory_input': sensory_input,
'language_input': sensory_input['text'],
'vla_output': vla_output,
'task_goal': task_goal,
'context': context,
'timestamp': time.time()
})
# 5. Reasoning and planning
reasoning_output = self.reasoning_engine.reason(
perceptual_understanding,
language_understanding,
vla_output,
task_goal,
context
)
# 6. Planning for longer-term goals
plan = self.planning_system.create_plan(
task_goal,
reasoning_output,
vla_output['actions']
)
# 7. Action execution
action_commands = self.action_interface.generate_commands(
vla_output['actions'],
plan,
reasoning_output
)
# 8. Learning from experience
self.learning_module.update_from_experience(
sensory_input,
vla_output,
action_commands,
task_goal,
reasoning_output
)
return {
'immediate_action': action_commands,
'reasoning_output': reasoning_output,
'generated_plan': plan,
'cognitive_state': {
'attention_weights': vla_output.get('person_attention_weights'),
'memory_recall': self.memory_system.recall_similar_episodes(sensory_input),
'learning_updates': self.learning_module.get_recent_updates()
}
}
class MemorySystem:
"""Memory system for cognitive VLA integration"""
def __init__(self):
self.episodic_memory = []
self.semantic_memory = {}
self.procedural_memory = {}
self.max_episodes = 1000
def store_episode(self, episode: Dict):
"""Store an episode in memory"""
self.episodic_memory.append(episode)
# Maintain memory size limit
if len(self.episodic_memory) > self.max_episodes:
self.episodic_memory.pop(0)
# Update semantic memory with learned patterns
self._update_semantic_memory(episode)
def _update_semantic_memory(self, episode: Dict):
"""Update semantic memory with learned patterns from episode"""
# Extract patterns from the episode
task = episode.get('task_goal', 'unknown')
context = episode.get('context', {})
if task not in self.semantic_memory:
self.semantic_memory[task] = {
'contexts': [],
'successful_patterns': [],
'failure_patterns': []
}
# Store context and outcome
self.semantic_memory[task]['contexts'].append(context)
def recall_similar_episodes(self, query: Dict) -> List[Dict]:
"""Recall episodes similar to the query"""
# Simple similarity-based recall (in practice, use more sophisticated methods)
similar_episodes = []
query_context = query.get('context', {})
query_task = query.get('task_goal', 'unknown')
for episode in self.episodic_memory[-50:]: # Check last 50 episodes
if (episode.get('task_goal') == query_task or
self._contexts_similar(episode.get('context', {}), query_context)):
similar_episodes.append(episode)
return similar_episodes
def _contexts_similar(self, ctx1: Dict, ctx2: Dict) -> bool:
"""Check if two contexts are similar"""
# Simple similarity check
common_keys = set(ctx1.keys()) & set(ctx2.keys())
if not common_keys:
return False
similarity_score = 0
for key in common_keys:
if ctx1[key] == ctx2[key]:
similarity_score += 1
return similarity_score / len(common_keys) > 0.5
class ReasoningEngine:
"""Reasoning engine for cognitive VLA integration"""
def __init__(self):
self.rule_base = self._initialize_rules()
self.inference_engine = InferenceEngine()
def _initialize_rules(self) -> List[Dict]:
"""Initialize reasoning rules"""
return [
{
'condition': lambda p, l, v, t, c: 'person_approaching' in p.get('detected_events', []),
'action': lambda: {'response': 'greet_person', 'priority': 'high'}
},
{
'condition': lambda p, l, v, t, c: 'help_request' in l.get('intent', ''),
'action': lambda: {'response': 'offer_assistance', 'priority': 'high'}
},
{
'condition': lambda p, l, v, t, c: c.get('time_of_day') == 'night' and 'move' in t.lower(),
'action': lambda: {'response': 'suggest_delay', 'priority': 'medium'}
}
]
def reason(self,
perceptual_understanding: Dict,
language_understanding: Dict,
vla_output: Dict,
task_goal: str,
context: Dict) -> Dict:
"""Perform reasoning based on inputs"""
reasoning_results = {
'inferences': [],
'suggestions': [],
'conflict_resolution': [],
'planning_constraints': []
}
# Apply rules
for rule in self.rule_base:
if rule['condition'](
perceptual_understanding,
language_understanding,
vla_output,
task_goal,
context
):
result = rule['action']()
reasoning_results['suggestions'].append(result)
# Perform more complex reasoning
reasoning_results['spatial_reasoning'] = self._spatial_reasoning(
perceptual_understanding, context
)
reasoning_results['social_reasoning'] = self._social_reasoning(
perceptual_understanding, context
)
reasoning_results['temporal_reasoning'] = self._temporal_reasoning(
task_goal, context
)
return reasoning_results
def _spatial_reasoning(self, perception: Dict, context: Dict) -> Dict:
"""Perform spatial reasoning"""
return {
'navigation_suggestions': [],
'obstacle_avoidance': [],
'personal_space_management': []
}
def _social_reasoning(self, perception: Dict, context: Dict) -> Dict:
"""Perform social reasoning"""
return {
'social_hierarchy': [],
'etiquette_compliance': [],
'group_dynamics': []
}
def _temporal_reasoning(self, task: str, context: Dict) -> Dict:
"""Perform temporal reasoning"""
return {
'task_sequencing': [],
'time_management': [],
'deadline_awareness': []
}
class PlanningSystem:
"""Planning system for cognitive VLA integration"""
def __init__(self):
self.hierarchical_planner = HierarchicalPlanner()
self.temporal_planner = TemporalPlanner()
self.resource_planner = ResourcePlanner()
def create_plan(self, task_goal: str, reasoning_output: Dict, initial_actions: torch.Tensor) -> Dict:
"""Create a plan based on task goal and reasoning"""
# Decompose task hierarchically
high_level_plan = self.hierarchical_planner.decompose_task(task_goal)
# Add temporal constraints
temporal_plan = self.temporal_planner.add_temporal_constraints(
high_level_plan,
reasoning_output
)
# Consider resources
resource_plan = self.resource_planner.allocate_resources(
temporal_plan,
reasoning_output
)
# Integrate with immediate actions
integrated_plan = self._integrate_immediate_actions(
resource_plan,
initial_actions
)
return {
'high_level_tasks': high_level_plan,
'temporal_constraints': temporal_plan,
'resource_allocations': resource_plan,
'integrated_plan': integrated_plan,
'execution_monitoring_points': self._get_monitoring_points(integrated_plan)
}
def _integrate_immediate_actions(self, plan: Dict, immediate_actions: torch.Tensor) -> Dict:
"""Integrate immediate VLA actions with higher-level plan"""
# This would integrate immediate actions with the plan
plan['immediate_actions'] = immediate_actions.tolist()
return plan
def _get_monitoring_points(self, plan: Dict) -> List[str]:
"""Get points where plan execution should be monitored"""
return ['start', 'critical_transition', 'end']
class LearningModule:
"""Learning module for cognitive VLA integration"""
def __init__(self):
self.experience_buffer = []
self.learning_algorithms = {
'supervised': SupervisedLearner(),
'reinforcement': ReinforcementLearner(),
'unsupervised': UnsupervisedLearner()
}
self.performance_tracker = PerformanceTracker()
def update_from_experience(self, sensory_input: Dict, vla_output: Dict, actions: Dict, task: str, reasoning: Dict):
"""Update learning systems from experience"""
experience = {
'sensory_input': sensory_input,
'vla_output': vla_output,
'actions_taken': actions,
'task_outcome': self._evaluate_outcome(actions, task),
'reasoning_used': reasoning,
'context': sensory_input.get('context', {}),
'timestamp': time.time()
}
self.experience_buffer.append(experience)
# Update different learning components
self._update_supervised_learning(experience)
self._update_reinforcement_learning(experience)
self._update_unsupervised_learning(experience)
# Track performance
self.performance_tracker.update_performance(experience)
def _evaluate_outcome(self, actions: Dict, task: str) -> Dict:
"""Evaluate the outcome of actions toward task"""
# This would evaluate success/failure of the task
return {
'success': np.random.random() > 0.3, # Simulated success
'task_progress': np.random.random(), # Progress toward task
'side_effects': [] # Any unintended consequences
}
def _update_supervised_learning(self, experience: Dict):
"""Update supervised learning from experience"""
# This would update models based on correct/incorrect actions
pass
def _update_reinforcement_learning(self, experience: Dict):
"""Update reinforcement learning from experience"""
# This would update policy based on rewards
pass
def _update_unsupervised_learning(self, experience: Dict):
"""Update unsupervised learning from experience"""
# This would discover patterns in the experience
pass
def get_recent_updates(self) -> Dict:
"""Get summary of recent learning updates"""
return {
'experiences_processed': len(self.experience_buffer[-10:]),
'performance_trend': self.performance_tracker.get_trend(),
'new_patterns_discovered': 0 # Would track discovered patterns
}
class PerceptionInterface:
"""Interface between sensors and cognitive systems"""
def __init__(self):
self.object_detector = ObjectDetector()
self.scene_analyzer = SceneAnalyzer()
self.social_perceptor = SocialPerceptor()
def process(self, visual_input: torch.Tensor, audio_input: torch.Tensor, context: Dict) -> Dict:
"""Process sensory input and return perceptual understanding"""
# Detect objects
objects = self.object_detector.detect(visual_input)
# Analyze scene
scene_analysis = self.scene_analyzer.analyze(visual_input, objects)
# Perceive social elements
social_perception = self.social_perceptor.perceive(visual_input, context)
return {
'objects': objects,
'scene_layout': scene_analysis,
'people': social_perception.get('people', []),
'social_cues': social_perception.get('cues', []),
'detected_events': social_perception.get('events', []),
'spatial_relationships': self._extract_spatial_relationships(objects, scene_analysis)
}
def _extract_spatial_relationships(self, objects: List[Dict], scene: Dict) -> List[Dict]:
"""Extract spatial relationships between objects"""
relationships = []
for i, obj1 in enumerate(objects):
for j, obj2 in enumerate(objects[i+1:], i+1):
relationship = {
'object1': obj1['id'],
'object2': obj2['id'],
'relationship': self._compute_relationship(obj1, obj2)
}
relationships.append(relationship)
return relationships
def _compute_relationship(self, obj1: Dict, obj2: Dict) -> str:
"""Compute spatial relationship between two objects"""
pos1 = obj1.get('position', [0, 0, 0])
pos2 = obj2.get('position', [0, 0, 0])
dx = pos2[0] - pos1[0]
dy = pos2[1] - pos1[1]
distance = np.sqrt(dx**2 + dy**2)
if distance < 0.5:
return 'very_close'
elif distance < 1.0:
return 'close'
elif distance < 2.0:
return 'near'
else:
return 'far'
class ActionInterface:
"""Interface between cognitive systems and actuators"""
def __init__(self):
self.motor_controller = MotorController()
self.social_controller = SocialController()
self.safety_checker = SafetyChecker()
def generate_commands(self, vla_actions: torch.Tensor, plan: Dict, reasoning: Dict) -> Dict:
"""Generate executable commands from VLA output and plan"""
# Check safety constraints
safe_actions = self.safety_checker.apply_safety_constraints(vla_actions, reasoning)
# Integrate with plan
planned_actions = self._integrate_with_plan(safe_actions, plan)
# Add social behaviors
social_enhanced_actions = self._add_social_behaviors(planned_actions, reasoning)
# Generate motor commands
motor_commands = self.motor_controller.generate_commands(social_enhanced_actions)
# Generate social commands
social_commands = self.social_controller.generate_commands(reasoning)
return {
'motor_commands': motor_commands,
'social_commands': social_commands,
'execution_plan': plan,
'safety_compliance': True
}
def _integrate_with_plan(self, actions: torch.Tensor, plan: Dict) -> torch.Tensor:
"""Integrate immediate actions with higher-level plan"""
# This would ensure immediate actions align with the plan
return actions
def _add_social_behaviors(self, actions: torch.Tensor, reasoning: Dict) -> torch.Tensor:
"""Add social behaviors to actions"""
# This would modify actions to include social considerations
return actions
class LanguageInterface:
"""Interface for language understanding and generation"""
def __init__(self):
self.speech_recognizer = SpeechRecognizer()
self.language_understander = LanguageUnderstander()
self.response_generator = ResponseGenerator()
def process(self, text_input: str, perceptual_context: Dict) -> Dict:
"""Process language input in perceptual context"""
# Understand the language input
understanding = self.language_understander.understand(text_input, perceptual_context)
# Generate appropriate response if needed
response = self.response_generator.generate_response(understanding, perceptual_context)
return {
'tokens': self._tokenize(text_input),
'intent': understanding.get('intent'),
'entities': understanding.get('entities'),
'contextual_meaning': understanding.get('contextual_meaning'),
'response_suggestion': response,
'confidence': understanding.get('confidence', 0.8)
}
def _tokenize(self, text: str) -> torch.Tensor:
"""Convert text to tokens"""
# Simple tokenization for demo
vocab = {'go': 1, 'to': 2, 'the': 3, 'kitchen': 4, 'help': 5, 'me': 6, 'please': 7}
tokens = [vocab.get(word, 0) for word in text.lower().split()[:20]]
tokens += [0] * (20 - len(tokens)) # Pad to 20
return torch.tensor(tokens, dtype=torch.long)
class HierarchicalVLAWithCognition:
"""Complete hierarchical VLA system with cognitive integration"""
def __init__(self):
# Create the VLA model
self.vla_model = MultiHumanVLA()
# Create cognitive integration
self.cognitive_integration = CognitiveVLAIntegration(self.vla_model)
# Task hierarchy manager
self.task_hierarchy = TaskHierarchyManager()
def execute_task(self, task_description: str, sensory_context: Dict) -> Dict:
"""Execute a task using the integrated cognitive-VLA system"""
# Determine task hierarchy level
hierarchy_level = self.task_hierarchy.determine_level(task_description)
# Process through cognitive cycle
cognitive_output = self.cognitive_integration.process_cognitive_cycle(
sensory_input=sensory_context,
task_goal=task_description,
context={'hierarchy_level': hierarchy_level, 'timestamp': time.time()}
)
return cognitive_output
def handle_social_interaction(self, interaction_type: str, people_context: List[Dict]) -> Dict:
"""Handle social interaction using cognitive-VLA integration"""
# Create appropriate sensory context
sensory_context = {
'visual': torch.randn(1, 3, 3, 224, 224), # Simulated visual input
'audio': torch.randn(1, 16000), # Simulated audio input
'text': f"Engage in {interaction_type}",
'proprioception': torch.randn(1, 28 * 2) # Robot state
}
# Execute social task
return self.execute_task(
task_description=f"perform {interaction_type} interaction",
sensory_context=sensory_context
)
class TaskHierarchyManager:
"""Manage task hierarchy for cognitive-VLA integration"""
def __init__(self):
self.task_levels = {
'high': ['navigate_to_room', 'find_person', 'initiate_interaction'],
'mid': ['greet_person', 'follow_person', 'hand_object'],
'low': ['joint_control', 'gripper_control', 'balance_maintenance']
}
def determine_level(self, task_description: str) -> str:
"""Determine appropriate hierarchy level for task"""
task_lower = task_description.lower()
for level, tasks in self.task_levels.items():
for task in tasks:
if task in task_lower:
return level
# Default to mid level for most social tasks
return 'mid'
def example_cognitive_vla_integration():
"""Example of cognitive VLA integration"""
# Create integrated system
system = HierarchicalVLAWithCognition()
# Simulated sensory context
sensory_context = {
'visual': torch.randn(1, 3, 3, 224, 224),
'audio': torch.randn(1, 16000),
'text': "Please greet the person in front of you",
'proprioception': torch.randn(1, 28 * 2)
}
# Execute a task
print("Executing 'greet person' task...")
result = system.execute_task("greet person", sensory_context)
print("Cognitive-VLA Integration Results:")
print(f"Immediate action shape: {result['immediate_action']['motor_commands'].shape}")
print(f"Reasoning inferences: {len(result['reasoning_output']['inferences'])}")
print(f"Generated plan steps: {len(result['generated_plan']['high_level_tasks'])}")
print(f"Memory episodes recalled: {len(result['cognitive_state']['memory_recall'])}")
# Handle a social interaction
print("\nHandling social interaction...")
people_context = [
{'id': 'person_1', 'distance': 1.0, 'orientation': 0.0},
{'id': 'person_2', 'distance': 2.0, 'orientation': 1.57}
]
social_result = system.handle_social_interaction("greeting", people_context)
print(f"Social interaction result - Motor commands shape: {social_result['immediate_action']['motor_commands'].shape}")
print(f"Social reasoning applied: {len(social_result['reasoning_output']['social_reasoning']) > 0}")
example_cognitive_vla_integration()
Advanced Social Behaviors and Etiquette
Implementing Social Norms and Etiquette
Humanoid robots must follow social norms and etiquette to be accepted in human environments. This requires sophisticated understanding and application of social rules.
class SocialNormsEngine:
"""Engine for managing social norms and etiquette"""
def __init__(self):
self.norms_database = self._load_social_norms()
self.etiquette_rules = self._load_etiquette_rules()
self.cultural_adaptor = CulturalAdaptor()
def _load_social_norms(self) -> Dict:
"""Load comprehensive database of social norms"""
return {
'personal_space': {
'intimate': 0.45, # meters
'personal': 1.2,
'social': 3.6,
'public': 7.5
},
'eye_contact': {
'duration_min': 0.3,
'duration_max': 3.0,
'frequency': 0.6 # Maintain 60% of conversation
},
'turn_taking': {
'pause_duration': 0.5, # seconds before responding
'interruption_penalty': 0.8
},
'proxemics': {
'front_approach': True, # Approach from front when possible
'side_movement': True, # Move to side when passing
'back_approach': False # Avoid approaching from behind
}
}
def _load_etiquette_rules(self) -> Dict:
"""Load etiquette rules for different situations"""
return {
'greeting_etiquette': [
'make_appropriate_eye_contact',
'smile_slightly',
'nod_head',
'use_appropriate_greeting_phrase',
'respect_personal_space'
],
'assistance_etiquette': [
'ask_permission_before_helping',
'explain_actions_before_performing',
'wait_for_acknowledgment',
'maintain_respectful_distance',
'offer_choices_when_appropriate'
],
'conversation_etiquette': [
'take_turns_speaking',
'show_active_listening',
'avoid_interruptions',
'maintain_topic_coherence',
'respect_cultural_differences'
]
}
def evaluate_social_compliance(self, actions: torch.Tensor, context: Dict) -> Dict:
"""Evaluate how well actions comply with social norms"""
compliance_score = 1.0
violations = []
# Check personal space maintenance
space_compliance = self._check_personal_space(actions, context)
if not space_compliance['compliant']:
violations.append('personal_space_violation')
compliance_score *= space_compliance['score']
# Check eye contact behavior
eye_contact_compliance = self._check_eye_contact(actions, context)
if not eye_contact_compliance['compliant']:
violations.append('eye_contact_violation')
compliance_score *= eye_contact_compliance['score']
# Check turn-taking behavior
turn_taking_compliance = self._check_turn_taking(actions, context)
if not turn_taking_compliance['compliant']:
violations.append('turn_taking_violation')
compliance_score *= turn_taking_compliance['score']
return {
'overall_compliance': compliance_score,
'violations': violations,
'detailed_compliance': {
'personal_space': space_compliance,
'eye_contact': eye_contact_compliance,
'turn_taking': turn_taking_compliance
}
}
def _check_personal_space(self, actions: torch.Tensor, context: Dict) -> Dict:
"""Check if actions maintain appropriate personal space"""
# This would check if movement actions respect personal space
# For simulation, assume 80% compliance
compliant = np.random.random() > 0.2
score = 0.8 if compliant else 0.3
return {
'compliant': compliant,
'score': score,
'details': 'Maintained appropriate distance in 80% of interactions'
}
def _check_eye_contact(self, actions: torch.Tensor, context: Dict) -> Dict:
"""Check if actions maintain appropriate eye contact"""
# This would check if gaze-related actions follow eye contact norms
compliant = np.random.random() > 0.3
score = 0.9 if compliant else 0.4
return {
'compliant': compliant,
'score': score,
'details': 'Maintained appropriate eye contact duration and frequency'
}
def _check_turn_taking(self, actions: torch.Tensor, context: Dict) -> Dict:
"""Check if actions follow turn-taking norms"""
compliant = np.random.random() > 0.25
score = 0.85 if compliant else 0.35
return {
'compliant': compliant,
'score': score,
'details': 'Followed appropriate turn-taking patterns'
}
def adapt_to_cultural_context(self, actions: torch.Tensor, culture_info: Dict) -> torch.Tensor:
"""Adapt actions based on cultural context"""
return self.cultural_adaptor.adapt_actions(actions, culture_info)
class CulturalAdaptor:
"""Adapt social behaviors to different cultural contexts"""
def __init__(self):
self.cultural_databases = self._load_cultural_databases()
def _load_cultural_databases(self) -> Dict:
"""Load cultural behavior databases"""
return {
'japanese': {
'bow_angle': 15, # degrees
'eye_contact_duration': 0.5, # seconds
'personal_space_multiplier': 1.2,
'formality_level': 'high',
'gesture_restrictions': ['avoid_direct_pointing']
},
'middle_eastern': {
'greeting_handshake': 'right_hand_only',
'personal_space_multiplier': 1.1,
'eye_contact_norms': 'moderate',
'gender_interaction_rules': 'conservative'
},
'mediterranean': {
'gesture_frequency': 'high',
'personal_space_multiplier': 0.8,
'physical_contact_norms': 'accepting',
'volume_modulation': 'variable'
},
'nordic': {
'personal_space_multiplier': 1.3,
'eye_contact_norms': 'direct',
'formality_level': 'moderate',
'silence_acceptance': 'high'
}
}
def adapt_actions(self, actions: torch.Tensor, culture_info: Dict) -> torch.Tensor:
"""Adapt actions based on cultural context"""
culture = culture_info.get('culture', 'default')
if culture in self.cultural_databases:
cultural_params = self.cultural_databases[culture]
adapted_actions = self._apply_cultural_modifications(actions, cultural_params)
else:
adapted_actions = actions # No cultural adaptation
return adapted_actions
def _apply_cultural_modifications(self, actions: torch.Tensor, params: Dict) -> torch.Tensor:
"""Apply cultural modifications to actions"""
# This would modify actions based on cultural parameters
# For simulation, just return the original actions with slight modification
modification_factor = torch.randn_like(actions) * 0.1
return actions + modification_factor
class EtiquetteManager:
"""Manage etiquette and social behavior execution"""
def __init__(self):
self.etiquette_engine = SocialNormsEngine()
self.behavior_selector = BehaviorSelector()
self.social_feedback_processor = SocialFeedbackProcessor()
def generate_etiquette_compliant_actions(self,
base_actions: torch.Tensor,
social_context: Dict,
interaction_type: str) -> torch.Tensor:
"""Generate actions that comply with etiquette for specific interaction"""
# Select appropriate etiquette rules
etiquette_rules = self.behavior_selector.select_etiquette_rules(
interaction_type, social_context
)
# Apply etiquette modifications to base actions
etiquette_compliant_actions = self._apply_etiquette_modifications(
base_actions, etiquette_rules, social_context
)
# Verify compliance
compliance_check = self.etiquette_engine.evaluate_social_compliance(
etiquette_compliant_actions, social_context
)
if compliance_check['overall_compliance'] < 0.7:
# If compliance is low, apply additional corrections
etiquette_compliant_actions = self._apply_compliance_corrections(
etiquette_compliant_actions, compliance_check, social_context
)
return etiquette_compliant_actions
def _apply_etiquette_modifications(self, actions: torch.Tensor, rules: List[str], context: Dict) -> torch.Tensor:
"""Apply etiquette rule modifications to actions"""
modified_actions = actions.clone()
# Apply modifications based on etiquette rules
for rule in rules:
if rule == 'maintain_personal_space':
modified_actions = self._modify_for_personal_space(modified_actions, context)
elif rule == 'make_appropriate_eye_contact':
modified_actions = self._modify_for_eye_contact(modified_actions, context)
elif rule == 'take_turns_speaking':
modified_actions = self._modify_for_turn_taking(modified_actions, context)
return modified_actions
def _modify_for_personal_space(self, actions: torch.Tensor, context: Dict) -> torch.Tensor:
"""Modify actions to maintain personal space"""
# This would adjust movement actions to respect personal space
return actions
def _modify_for_eye_contact(self, actions: torch.Tensor, context: Dict) -> torch.Tensor:
"""Modify actions to maintain appropriate eye contact"""
# This would adjust gaze-related actions
return actions
def _modify_for_turn_taking(self, actions: torch.Tensor, context: Dict) -> torch.Tensor:
"""Modify actions to follow turn-taking norms"""
# This would adjust timing and response actions
return actions
def _apply_compliance_corrections(self, actions: torch.Tensor, compliance: Dict, context: Dict) -> torch.Tensor:
"""Apply corrections to improve social compliance"""
corrected_actions = actions.clone()
# Apply corrections based on specific violations
for violation in compliance['violations']:
if violation == 'personal_space_violation':
corrected_actions = self._correct_personal_space(corrected_actions, context)
elif violation == 'eye_contact_violation':
corrected_actions = self._correct_eye_contact(corrected_actions, context)
return corrected_actions
def _correct_personal_space(self, actions: torch.Tensor, context: Dict) -> torch.Tensor:
"""Apply correction for personal space violation"""
return actions
def _correct_eye_contact(self, actions: torch.Tensor, context: Dict) -> torch.Tensor:
"""Apply correction for eye contact violation"""
return actions
class BehaviorSelector:
"""Select appropriate behaviors based on context"""
def __init__(self):
self.behavior_database = self._create_behavior_database()
def _create_behavior_database(self) -> Dict:
"""Create database of context-appropriate behaviors"""
return {
'greeting': {
'formal': ['bow', 'shake_hands', 'maintain_3ft_distance'],
'informal': ['wave', 'smile', 'maintain_2ft_distance'],
'cultural': {
'japanese': ['deep_bow', 'no_handshake', 'respectful_distance'],
'middle_eastern': ['hand_on_heart', 'respectful_greeting', 'moderate_distance']
}
},
'assistance': {
'elderly': ['speak_clearly', 'move_slowly', 'offer_steady_support'],
'child': ['crouch_down', 'speak_loudly', 'use_simple_language'],
'disabled': ['ask_permission', 'provide_options', 'maintain_patience']
},
'conversation': {
'group': ['maintain_awareness', 'rotate_attention', 'respect_turns'],
'one_on_one': ['focus_attention', 'maintain_eye_contact', 'show_engagement']
}
}
def select_etiquette_rules(self, interaction_type: str, context: Dict) -> List[str]:
"""Select etiquette rules based on interaction type and context"""
rules = []
# Get base rules for interaction type
if interaction_type in self.behavior_database:
base_rules = self.behavior_database[interaction_type]
# Apply context-specific modifications
if 'formality' in context:
formality_level = context['formality']
if formality_level in base_rules:
rules.extend(base_rules[formality_level])
# Apply cultural modifications
if 'culture' in context:
culture = context['culture']
if 'cultural' in base_rules and culture in base_rules['cultural']:
rules.extend(base_rules['cultural'][culture])
return rules
class SocialFeedbackProcessor:
"""Process social feedback to improve behavior"""
def __init__(self):
self.feedback_memory = []
self.improvement_engine = ImprovementEngine()
def process_social_feedback(self, feedback: Dict, actions: torch.Tensor, context: Dict) -> Dict:
"""Process social feedback and suggest improvements"""
feedback_analysis = {
'feedback_type': feedback.get('type', 'neutral'),
'intensity': feedback.get('intensity', 0.5),
'specificity': feedback.get('specificity', 'general'),
'cultural_context': context.get('culture', 'default')
}
# Store feedback for learning
self.feedback_memory.append({
'feedback': feedback,
'actions': actions,
'context': context,
'timestamp': time.time()
})
# Analyze feedback and suggest improvements
improvements = self.improvement_engine.analyze_feedback_and_suggest_improvements(
feedback_analysis, actions, context
)
return {
'analysis': feedback_analysis,
'suggested_improvements': improvements,
'behavioral_adjustments': self._calculate_behavioral_adjustments(improvements),
'learning_opportunities': self._identify_learning_opportunities(feedback, actions)
}
def _calculate_behavioral_adjustments(self, improvements: List[Dict]) -> Dict:
"""Calculate specific behavioral adjustments"""
adjustments = {}
for improvement in improvements:
aspect = improvement.get('aspect', 'general')
magnitude = improvement.get('magnitude', 0.1)
adjustments[aspect] = magnitude
return adjustments
def _identify_learning_opportunities(self, feedback: Dict, actions: torch.Tensor) -> List[str]:
"""Identify opportunities for learning from feedback"""
opportunities = []
if feedback.get('type') == 'negative':
opportunities.append('avoid_similar_actions')
elif feedback.get('type') == 'positive':
opportunities.append('reinforce_successful_patterns')
if feedback.get('specificity') == 'specific':
opportunities.append('targeted_behavior_learning')
return opportunities
class ImprovementEngine:
"""Engine for analyzing feedback and suggesting improvements"""
def __init__(self):
self.improvement_rules = self._load_improvement_rules()
def _load_improvement_rules(self) -> List[Dict]:
"""Load rules for generating improvement suggestions"""
return [
{
'condition': lambda f, a, c: f.get('type') == 'negative' and f.get('intensity', 0) > 0.7,
'action': lambda: {'aspect': 'action_selection', 'magnitude': 0.3, 'reason': 'strong_negative_feedback'}
},
{
'condition': lambda f, a, c: f.get('type') == 'positive' and f.get('intensity', 0) > 0.8,
'action': lambda: {'aspect': 'confidence_boost', 'magnitude': 0.1, 'reason': 'strong_positive_feedback'}
},
{
'condition': lambda f, a, c: c.get('culture') == 'japanese' and f.get('type') == 'negative',
'action': lambda: {'aspect': 'cultural_sensitivity', 'magnitude': 0.4, 'reason': 'cultural_norm_violation'}
}
]
def analyze_feedback_and_suggest_improvements(self, feedback_analysis: Dict, actions: torch.Tensor, context: Dict) -> List[Dict]:
"""Analyze feedback and suggest specific improvements"""
suggestions = []
for rule in self.improvement_rules:
if rule['condition'](feedback_analysis, actions, context):
suggestion = rule['action']()
suggestions.append(suggestion)
# Add default suggestions if no specific ones apply
if not suggestions:
suggestions.append({
'aspect': 'general_behavior',
'magnitude': 0.1,
'reason': 'neutral_feedback_context'
})
return suggestions
def example_etiquette_integration():
"""Example of etiquette and social norms integration"""
# Create etiquette manager
etiquette_manager = EtiquetteManager()
# Simulate base actions from VLA model
base_actions = torch.randn(1, 28) # 28 joint positions
# Define social context
social_context = {
'culture': 'japanese',
'formality': 'formal',
'interaction_type': 'greeting',
'number_of_people': 1,
'age_group': 'adult',
'setting': 'office'
}
# Generate etiquette-compliant actions
etiquette_actions = etiquette_manager.generate_etiquette_compliant_actions(
base_actions,
social_context,
'greeting'
)
print("Etiquette Integration Results:")
print(f"Original actions norm: {torch.norm(base_actions).item():.3f}")
print(f"Etiquette-compliant actions norm: {torch.norm(etiquette_actions).item():.3f}")
print(f"Action modification magnitude: {torch.norm(etiquette_actions - base_actions).item():.3f}")
# Process social feedback
feedback = {
'type': 'positive',
'intensity': 0.9,
'specificity': 'specific',
'cultural_context': 'japanese'
}
feedback_processor = SocialFeedbackProcessor()
feedback_result = feedback_processor.process_social_feedback(
feedback, etiquette_actions, social_context
)
print(f"\nFeedback Processing Results:")
print(f"Feedback analysis: {feedback_result['analysis']}")
print(f"Suggested improvements: {len(feedback_result['suggested_improvements'])}")
print(f"Behavioral adjustments: {feedback_result['behavioral_adjustments']}")
example_etiquette_integration()
Collaborative Human-Robot Interaction
Advanced Collaborative Behaviors
Humanoid robots designed for VLA applications often need to work collaboratively with humans, requiring advanced understanding of human intentions, coordination, and shared task execution.
class CollaborativeInteractionManager:
"""Manage collaborative interactions between humans and robots"""
def __init__(self):
self.intention_predictor = IntentionPredictor()
self.coordination_manager = CoordinationManager()
self.shared_attention = SharedAttentionMechanism()
self.task_decomposer = TaskDecomposer()
self.safety_coordinator = SafetyCoordinator()
def initiate_collaboration(self,
human_intent: str,
environmental_context: Dict,
robot_capabilities: Dict) -> Dict:
"""Initiate collaborative interaction based on human intent"""
# Predict human intentions
predicted_intentions = self.intention_predictor.predict(
human_intent, environmental_context
)
# Determine optimal task decomposition
task_decomposition = self.task_decomposer.decompose(
predicted_intentions, robot_capabilities, environmental_context
)
# Establish shared attention
attention_alignment = self.shared_attention.align(
environmental_context, predicted_intentions
)
# Coordinate actions
coordination_plan = self.coordination_manager.plan(
task_decomposition, attention_alignment, environmental_context
)
# Ensure safety
safety_guarantees = self.safety_coordinator.establish_guarantees(
coordination_plan, environmental_context
)
return {
'predicted_intentions': predicted_intentions,
'task_decomposition': task_decomposition,
'attention_alignment': attention_alignment,
'coordination_plan': coordination_plan,
'safety_guarantees': safety_guarantees,
'collaboration_readiness': self._assess_collaboration_readiness(
coordination_plan, safety_guarantees
)
}
def _assess_collaboration_readiness(self, plan: Dict, safety: Dict) -> bool:
"""Assess if collaboration can proceed safely"""
return (len(plan.get('robot_tasks', [])) > 0 and
safety.get('collision_free', True) and
safety.get('emergency_stops_available', True))
def execute_collaborative_step(self,
human_action: Dict,
robot_state: Dict,
collaboration_context: Dict) -> Dict:
"""Execute one step of collaborative interaction"""
# Interpret human action
human_action_interpretation = self._interpret_human_action(
human_action, collaboration_context
)
# Update shared understanding
updated_context = self._update_collaboration_context(
collaboration_context, human_action_interpretation
)
# Generate coordinated response
robot_response = self._generate_coordinated_response(
human_action, robot_state, updated_context
)
# Verify safety
is_safe = self.safety_coordinator.verify_safety(
robot_response, human_action, updated_context
)
return {
'robot_response': robot_response,
'safety_verification': is_safe,
'updated_context': updated_context,
'collaboration_progress': self._calculate_progress(
updated_context, collaboration_context
)
}
def _interpret_human_action(self, action: Dict, context: Dict) -> Dict:
"""Interpret human action in collaborative context"""
return {
'action_type': action.get('type', 'unknown'),
'intention': action.get('intention', 'unknown'),
'target_object': action.get('target_object'),
'spatial_reference': action.get('spatial_reference'),
'collaboration_implication': self._infer_collaboration_implication(action, context)
}
def _infer_collaboration_implication(self, action: Dict, context: Dict) -> str:
"""Infer what the human action implies for collaboration"""
# This would use more sophisticated reasoning
return "continue_collaboration"
def _update_collaboration_context(self, context: Dict, interpretation: Dict) -> Dict:
"""Update collaboration context based on human action"""
updated = context.copy()
updated['last_human_action'] = interpretation
updated['collaboration_state'] = self._update_collaboration_state(
context.get('collaboration_state', 'initial'), interpretation
)
return updated
def _update_collaboration_state(self, current_state: str, action_interpretation: Dict) -> str:
"""Update collaboration state based on action"""
# State machine for collaboration
state_transitions = {
('initial', 'reaching_for_object'): 'object_transfer',
('object_transfer', 'releasing_object'): 'task_continuation',
('task_continuation', 'requesting_assistance'): 'assistance_provision',
('assistance_provision', 'acknowledging_help'): 'task_continuation'
}
action_type = action_interpretation.get('action_type', 'unknown')
transition_key = (current_state, action_type)
return state_transitions.get(transition_key, current_state)
def _generate_coordinated_response(self, human_action: Dict, robot_state: Dict, context: Dict) -> Dict:
"""Generate robot response coordinated with human action"""
response_type = self._determine_response_type(human_action, context)
if response_type == 'complementary':
return self._generate_complementary_action(human_action, robot_state, context)
elif response_type == 'supportive':
return self._generate_supportive_action(human_action, robot_state, context)
elif response_type == 'anticipatory':
return self._generate_anticipatory_action(human_action, robot_state, context)
else:
return {'type': 'monitoring', 'action': 'continue_attention'}
def _determine_response_type(self, human_action: Dict, context: Dict) -> str:
"""Determine appropriate response type for human action"""
action_type = human_action.get('type', 'unknown')
response_mapping = {
'reaching': 'complementary',
'lifting': 'supportive',
'moving': 'anticipatory',
'stopping': 'supportive',
'requesting': 'complementary'
}
return response_mapping.get(action_type, 'monitoring')
def _generate_complementary_action(self, human_action: Dict, robot_state: Dict, context: Dict) -> Dict:
"""Generate action that complements human action"""
return {
'type': 'complementary',
'action': 'position_for_object_transfer',
'parameters': {
'target_position': self._calculate_complementary_position(human_action, robot_state)
}
}
def _generate_supportive_action(self, human_action: Dict, robot_state: Dict, context: Dict) -> Dict:
"""Generate action that supports human action"""
return {
'type': 'supportive',
'action': 'provide_stability_support',
'parameters': {
'support_position': self._calculate_support_position(human_action, robot_state)
}
}
def _generate_anticipatory_action(self, human_action: Dict, robot_state: Dict, context: Dict) -> Dict:
"""Generate action that anticipates human needs"""
return {
'type': 'anticipatory',
'action': 'prepare_next_tool',
'parameters': {
'predicted_next_action': self._predict_next_human_action(human_action, context)
}
}
def _calculate_complementary_position(self, human_action: Dict, robot_state: Dict) -> List[float]:
"""Calculate complementary position for robot"""
# Calculate position that complements human action
human_pos = human_action.get('position', [0, 0, 0])
return [human_pos[0] + 0.5, human_pos[1], human_pos[2]] # Offset by 0.5m
def _calculate_support_position(self, human_action: Dict, robot_state: Dict) -> List[float]:
"""Calculate support position for robot"""
return [0, 0, 0] # Placeholder
def _predict_next_human_action(self, human_action: Dict, context: Dict) -> str:
"""Predict human's next likely action"""
return "unknown" # Placeholder
def _calculate_progress(self, new_context: Dict, old_context: Dict) -> float:
"""Calculate progress in collaboration"""
return 0.5 # Placeholder
class IntentionPredictor:
"""Predict human intentions in collaborative scenarios"""
def __init__(self):
self.intention_models = self._load_intention_models()
def _load_intention_models(self) -> Dict:
"""Load models for predicting different types of intentions"""
return {
'object_interaction': self._create_object_interaction_model(),
'spatial_navigation': self._create_spatial_model(),
'social_communication': self._create_social_model(),
'task_completion': self._create_task_model()
}
def _create_object_interaction_model(self):
"""Create model for predicting object interaction intentions"""
# This would be a trained model in practice
return lambda action, context: self._predict_object_intention(action, context)
def _create_spatial_model(self):
"""Create model for predicting spatial intentions"""
return lambda action, context: self._predict_spatial_intention(action, context)
def _create_social_model(self):
"""Create model for predicting social intentions"""
return lambda action, context: self._predict_social_intention(action, context)
def _create_task_model(self):
"""Create model for predicting task intentions"""
return lambda action, context: self._predict_task_intention(action, context)
def predict(self, observed_action: str, context: Dict) -> Dict:
"""Predict human intentions based on observed action and context"""
predictions = {}
# Predict across different intention types
for intent_type, model in self.intention_models.items():
predictions[intent_type] = model(observed_action, context)
# Combine predictions with confidence scores
combined_prediction = self._combine_predictions(predictions)
return combined_prediction
def _predict_object_intention(self, action: str, context: Dict) -> Dict:
"""Predict object-related intentions"""
# Simple rule-based prediction for demo
if 'grasp' in action.lower() or 'pick' in action.lower():
return {
'intention': 'grasping_object',
'target_object': context.get('nearest_object', 'unknown'),
'confidence': 0.9
}
elif 'place' in action.lower() or 'put' in action.lower():
return {
'intention': 'placing_object',
'target_location': context.get('available_surface', 'unknown'),
'confidence': 0.85
}
else:
return {'intention': 'none', 'confidence': 0.1}
def _predict_spatial_intention(self, action: str, context: Dict) -> Dict:
"""Predict spatial intentions"""
return {
'intention': 'moving_to_location',
'target_location': context.get('target_area', 'unknown'),
'confidence': 0.7
}
def _predict_social_intention(self, action: str, context: Dict) -> Dict:
"""Predict social intentions"""
return {
'intention': 'initiating_interaction',
'target_person': context.get('interlocutor', 'unknown'),
'confidence': 0.6
}
def _predict_task_intention(self, action: str, context: Dict) -> Dict:
"""Predict task-related intentions"""
return {
'intention': 'completing_task_step',
'task_name': context.get('current_task', 'unknown'),
'confidence': 0.8
}
def _combine_predictions(self, predictions: Dict) -> Dict:
"""Combine multiple intention predictions"""
# Select the highest confidence prediction or combine them
best_prediction = max(predictions.values(), key=lambda x: x.get('confidence', 0))
return {
'primary_intention': best_prediction,
'all_predictions': predictions,
'overall_confidence': best_prediction.get('confidence', 0.0)
}
class CoordinationManager:
"""Manage coordination between human and robot actions"""
def __init__(self):
self.coordination_strategies = self._define_coordination_strategies()
self.timing_optimizer = TimingOptimizer()
def _define_coordination_strategies(self) -> Dict:
"""Define different coordination strategies"""
return {
'parallel': {
'description': 'Humans and robots work simultaneously on different aspects',
'requirements': ['non_interfering_tasks', 'clear_boundaries'],
'benefits': ['speed', 'efficiency']
},
'sequential': {
'description': 'Humans and robots take turns based on expertise',
'requirements': ['well_defined_handoffs', 'clear_signals'],
'benefits': ['precision', 'safety']
},
'collaborative': {
'description': 'Humans and robots work together on the same task',
'requirements': ['synchronized_actions', 'shared_attention'],
'benefits': ['complexity_handling', 'adaptability']
}
}
def plan(self, task_decomposition: Dict, attention_alignment: Dict, context: Dict) -> Dict:
"""Plan coordination strategy for the task"""
# Select appropriate coordination strategy
strategy = self._select_coordination_strategy(task_decomposition, context)
# Optimize timing
timing_plan = self.timing_optimizer.optimize(
strategy, task_decomposition, context
)
# Generate coordination commands
coordination_commands = self._generate_coordination_commands(
strategy, timing_plan, task_decomposition
)
return {
'strategy': strategy,
'timing_plan': timing_plan,
'coordination_commands': coordination_commands,
'synchronization_points': self._identify_synchronization_points(task_decomposition)
}
def _select_coordination_strategy(self, task_decomposition: Dict, context: Dict) -> str:
"""Select the most appropriate coordination strategy"""
# Simple selection logic for demo
task_complexity = len(task_decomposition.get('subtasks', []))
human_expertise = context.get('human_expertise', 'medium')
safety_requirements = context.get('safety_requirements', 'standard')
if safety_requirements == 'high':
return 'sequential' # Safer to take turns
elif task_complexity > 5 or human_expertise == 'low':
return 'collaborative' # Work together for complex tasks
else:
return 'parallel' # Efficient for simple tasks
def _generate_coordination_commands(self, strategy: str, timing: Dict, task_decomp: Dict) -> List[Dict]:
"""Generate specific coordination commands"""
commands = []
if strategy == 'parallel':
commands = self._generate_parallel_commands(task_decomp)
elif strategy == 'sequential':
commands = self._generate_sequential_commands(task_decomp)
elif strategy == 'collaborative':
commands = self._generate_collaborative_commands(task_decomp)
return commands
def _generate_parallel_commands(self, task_decomp: Dict) -> List[Dict]:
"""Generate commands for parallel coordination"""
commands = []
robot_tasks = task_decomp.get('robot_tasks', [])
human_tasks = task_decomp.get('human_tasks', [])
for i, (robot_task, human_task) in enumerate(zip(robot_tasks, human_tasks)):
commands.append({
'step': i,
'robot_action': robot_task,
'human_action': human_task,
'simultaneous': True,
'safety_boundary': 'maintained'
})
return commands
def _generate_sequential_commands(self, task_decomp: Dict) -> List[Dict]:
"""Generate commands for sequential coordination"""
commands = []
all_tasks = task_decomp.get('ordered_tasks', [])
for i, task in enumerate(all_tasks):
commands.append({
'step': i,
'actor': 'robot' if i % 2 == 0 else 'human',
'action': task,
'handoff_signal': 'proceed' if i > 0 else 'start',
'completion_check': True
})
return commands
def _generate_collaborative_commands(self, task_decomp: Dict) -> List[Dict]:
"""Generate commands for collaborative coordination"""
commands = []
collaborative_tasks = task_decomp.get('collaborative_tasks', [])
for i, task in enumerate(collaborative_tasks):
commands.append({
'step': i,
'robot_role': task.get('robot_role', 'support'),
'human_role': task.get('human_role', 'lead'),
'synchronized_action': True,
'feedback_required': True
})
return commands
def _identify_synchronization_points(self, task_decomposition: Dict) -> List[int]:
"""Identify points where synchronization is needed"""
# This would analyze task dependencies
return [0, len(task_decomposition.get('subtasks', [])) // 2] # Example points
class SharedAttentionMechanism:
"""Mechanism for establishing and maintaining shared attention"""
def __init__(self):
self.attention_models = self._create_attention_models()
def _create_attention_models(self) -> Dict:
"""Create models for different types of attention"""
return {
'object_attention': ObjectAttentionModel(),
'spatial_attention': SpatialAttentionModel(),
'social_attention': SocialAttentionModel()
}
def align(self, environmental_context: Dict, predicted_intentions: Dict) -> Dict:
"""Align robot attention with human attention and intentions"""
alignment_results = {}
# Align object attention
object_alignment = self.attention_models['object_attention'].align(
environmental_context, predicted_intentions
)
alignment_results['object_attention'] = object_alignment
# Align spatial attention
spatial_alignment = self.attention_models['spatial_attention'].align(
environmental_context, predicted_intentions
)
alignment_results['spatial_attention'] = spatial_alignment
# Align social attention
social_alignment = self.attention_models['social_attention'].align(
environmental_context, predicted_intentions
)
alignment_results['social_attention'] = social_alignment
# Calculate overall alignment score
alignment_results['overall_alignment'] = self._calculate_alignment_score(alignment_results)
return alignment_results
def _calculate_alignment_score(self, alignment_results: Dict) -> float:
"""Calculate overall attention alignment score"""
scores = []
for key, value in alignment_results.items():
if key != 'overall_alignment' and 'alignment_score' in value:
scores.append(value['alignment_score'])
return np.mean(scores) if scores else 0.5
class ObjectAttentionModel(nn.Module):
"""Model for object-focused shared attention"""
def __init__(self):
super().__init__()
self.object_detector = nn.Linear(256, 64) # Simplified object detection
self.attention_predictor = nn.Linear(64, 1)
def align(self, env_context: Dict, intentions: Dict) -> Dict:
"""Align object attention"""
# This would process visual input and intention to focus on relevant objects
relevant_objects = env_context.get('objects', [])
target_object = intentions.get('object_interaction', {}).get('target_object', 'unknown')
# Calculate attention weights for objects
attention_weights = {}
for obj in relevant_objects:
obj_id = obj.get('id', 'unknown')
if obj_id == target_object:
attention_weights[obj_id] = 0.9
else:
attention_weights[obj_id] = 0.1
return {
'target_object': target_object,
'attention_weights': attention_weights,
'alignment_score': 0.85
}
class SpatialAttentionModel(nn.Module):
"""Model for spatial shared attention"""
def __init__(self):
super().__init__()
def align(self, env_context: Dict, intentions: Dict) -> Dict:
"""Align spatial attention"""
target_location = env_context.get('target_location', [0, 0, 0])
attention_region = self._define_attention_region(target_location, env_context)
return {
'target_location': target_location,
'attention_region': attention_region,
'alignment_score': 0.8
}
def _define_attention_region(self, target: List[float], context: Dict) -> Dict:
"""Define region of spatial attention"""
return {
'center': target,
'radius': 1.0, # meter
'importance': 'high'
}
class SocialAttentionModel(nn.Module):
"""Model for social shared attention"""
def __init__(self):
super().__init__()
def align(self, env_context: Dict, intentions: Dict) -> Dict:
"""Align social attention"""
focus_person = env_context.get('focus_person', 'unknown')
social_context = env_context.get('social_context', 'neutral')
return {
'focus_person': focus_person,
'social_context': social_context,
'alignment_score': 0.75
}
class TaskDecomposer:
"""Decompose tasks for human-robot collaboration"""
def __init__(self):
self.decomposition_rules = self._define_decomposition_rules()
def _define_decomposition_rules(self) -> Dict:
"""Define rules for task decomposition"""
return {
'by_capability': self._decompose_by_capability,
'by_safety': self._decompose_by_safety,
'by_efficiency': self._decompose_by_efficiency,
'by_preference': self._decompose_by_preference
}
def decompose(self, intentions: Dict, capabilities: Dict, context: Dict) -> Dict:
"""Decompose task based on multiple criteria"""
# Apply different decomposition strategies
capability_decomp = self.decomposition_rules['by_capability'](intentions, capabilities, context)
safety_decomp = self.decomposition_rules['by_safety'](intentions, capabilities, context)
efficiency_decomp = self.decomposition_rules['by_efficiency'](intentions, capabilities, context)
preference_decomp = self.decomposition_rules['by_preference'](intentions, capabilities, context)
# Integrate decompositions
integrated_decomposition = self._integrate_decompositions([
capability_decomp, safety_decomp, efficiency_decomp, preference_decomp
])
return integrated_decomposition
def _decompose_by_capability(self, intentions: Dict, capabilities: Dict, context: Dict) -> Dict:
"""Decompose task based on capabilities"""
human_tasks = []
robot_tasks = []
# Assign tasks based on capabilities
for subtask in self._extract_subtasks(intentions):
if self._is_better_for_human(subtask, capabilities):
human_tasks.append(subtask)
else:
robot_tasks.append(subtask)
return {
'human_tasks': human_tasks,
'robot_tasks': robot_tasks,
'method': 'capability_based'
}
def _decompose_by_safety(self, intentions: Dict, capabilities: Dict, context: Dict) -> Dict:
"""Decompose task based on safety considerations"""
safe_for_robot = []
safe_for_human = []
require_supervision = []
for subtask in self._extract_subtasks(intentions):
safety_level = self._assess_safety(subtask, context)
if safety_level == 'robot_safe':
safe_for_robot.append(subtask)
elif safety_level == 'human_safe':
safe_for_human.append(subtask)
else:
require_supervision.append(subtask)
return {
'robot_tasks': safe_for_robot,
'human_tasks': safe_for_human,
'supervised_tasks': require_supervision,
'method': 'safety_based'
}
def _decompose_by_efficiency(self, intentions: Dict, capabilities: Dict, context: Dict) -> Dict:
"""Decompose task based on efficiency"""
# This would optimize for time, energy, or other efficiency metrics
return {
'optimized_tasks': self._extract_subtasks(intentions),
'method': 'efficiency_based'
}
def _decompose_by_preference(self, intentions: Dict, capabilities: Dict, context: Dict) -> Dict:
"""Decompose task based on human preferences"""
# This would consider human preferences expressed or inferred
return {
'preferred_allocation': self._extract_subtasks(intentions),
'method': 'preference_based'
}
def _integrate_decompositions(self, decompositions: List[Dict]) -> Dict:
"""Integrate multiple decomposition results"""
integrated = {
'subtasks': [],
'actor_assignments': {},
'coordination_points': [],
'safety_considerations': []
}
# For simplicity, we'll use the first decomposition as primary
# In practice, this would be more sophisticated
if decompositions:
primary = decompositions[0]
integrated.update(primary)
return integrated
def _extract_subtasks(self, intentions: Dict) -> List[str]:
"""Extract subtasks from intentions"""
# This would decompose high-level intentions into concrete subtasks
return ['subtask_1', 'subtask_2', 'subtask_3'] # Placeholder
def _is_better_for_human(self, subtask: str, capabilities: Dict) -> bool:
"""Determine if subtask is better performed by human"""
# This would compare human vs robot capabilities for the subtask
return np.random.random() > 0.5 # Random for demo
def _assess_safety(self, subtask: str, context: Dict) -> str:
"""Assess safety level for subtask"""
return 'robot_safe' # Placeholder
class SafetyCoordinator:
"""Coordinate safety in human-robot collaboration"""
def __init__(self):
self.safety_protocols = self._define_safety_protocols()
self.collision_detector = CollisionDetector()
self.emergency_handler = EmergencyHandler()
def _define_safety_protocols(self) -> Dict:
"""Define safety protocols for collaboration"""
return {
'collision_avoidance': {
'minimum_distance': 0.5, # meters
'prediction_horizon': 2.0 # seconds
},
'force_limiting': {
'max_force': 50.0, # Newtons
'max_torque': 20.0 # Nm
},
'emergency_stop': {
'response_time': 0.1, # seconds
'activation_methods': ['human_request', 'sensor_fusion', 'model_prediction']
}
}
def establish_guarantees(self, coordination_plan: Dict, context: Dict) -> Dict:
"""Establish safety guarantees for the coordination plan"""
# Check collision safety
collision_safety = self._check_collision_safety(coordination_plan, context)
# Check force safety
force_safety = self._check_force_safety(coordination_plan, context)
# Establish emergency protocols
emergency_protocols = self._establish_emergency_protocols(coordination_plan)
return {
'collision_free': collision_safety['safe'],
'force_safe': force_safety['safe'],
'emergency_protocols': emergency_protocols,
'safety_confidence': self._calculate_safety_confidence([
collision_safety, force_safety
])
}
def _check_collision_safety(self, plan: Dict, context: Dict) -> Dict:
"""Check if plan is collision-safe"""
# This would run collision detection algorithms
safe = np.random.random() > 0.1 # 90% safe for demo
return {
'safe': safe,
'collision_risk': 0.1 if safe else 0.9,
'safety_margin': 0.6 if safe else 0.2
}
def _check_force_safety(self, plan: Dict, context: Dict) -> Dict:
"""Check if plan respects force limits"""
safe = np.random.random() > 0.05 # 95% safe for demo
return {
'safe': safe,
'max_force_applied': 15.0 if safe else 60.0,
'force_margin': 0.7 if safe else 0.1
}
def _establish_emergency_protocols(self, plan: Dict) -> List[str]:
"""Establish emergency protocols for the plan"""
return ['collision_emergency_stop', 'force_limit_exceeded', 'human_override']
def _calculate_safety_confidence(self, safety_checks: List[Dict]) -> float:
"""Calculate overall safety confidence"""
if not safety_checks:
return 0.5
confidences = [check.get('safety_margin', 0.5) for check in safety_checks]
return np.mean(confidences)
def verify_safety(self, robot_action: Dict, human_action: Dict, context: Dict) -> bool:
"""Verify safety of proposed actions"""
# Check for potential collisions
collision_risk = self.collision_detector.assess_risk(robot_action, human_action, context)
# Check force constraints
force_safe = self._is_force_safe(robot_action, context)
# Overall safety check
return collision_risk < 0.1 and force_safe
def _is_force_safe(self, action: Dict, context: Dict) -> bool:
"""Check if action respects force limits"""
return True # Placeholder
class CollisionDetector:
"""Detect potential collisions in human-robot interaction"""
def __init__(self):
self.prediction_model = self._create_prediction_model()
def assess_risk(self, robot_action: Dict, human_action: Dict, context: Dict) -> float:
"""Assess collision risk between robot and human actions"""
# This would use trajectory prediction and collision checking
return np.random.random() * 0.2 # Random risk for demo (0-20%)
def _create_prediction_model(self):
"""Create model for predicting future positions"""
pass
class EmergencyHandler:
"""Handle emergency situations in collaboration"""
def __init__(self):
self.emergency_protocols = self._define_emergency_protocols()
def _define_emergency_protocols(self) -> Dict:
"""Define emergency response protocols"""
return {
'immediate_stop': {
'trigger': ['collision_imminent', 'force_limit_exceeded', 'human_distress'],
'action': 'full_motor_stop',
'time_limit': 0.1
},
'safe_position': {
'trigger': ['moderate_risk', 'uncertainty_high'],
'action': 'move_to_safe_position',
'time_limit': 0.5
},
'request_assistance': {
'trigger': ['multiple_failures', 'unhandled_situation'],
'action': 'request_human_supervisor',
'time_limit': 1.0
}
}
def example_collaborative_interaction():
"""Example of collaborative human-robot interaction"""
# Create collaboration manager
collab_manager = CollaborativeInteractionManager()
# Define human intent and context
human_intent = "assemble_widget_with_robot_assistance"
environmental_context = {
'objects': [
{'id': 'widget_base', 'position': [0.5, 0.0, 0.1], 'type': 'assembly_base'},
{'id': 'screwdriver', 'position': [0.6, 0.0, 0.1], 'type': 'tool'},
{'id': 'screws', 'position': [0.7, 0.0, 0.1], 'type': 'fasteners'}
],
'target_location': [0.5, 0.0, 0.1],
'workspace_constraints': {'height': 1.0, 'width': 0.8},
'safety_requirements': 'standard'
}
robot_capabilities = {
'precision_manipulation': True,
'force_control': True,
'visual_servoing': True,
'collision_detection': True
}
# Initiate collaboration
print("Initiating collaboration...")
collaboration_setup = collab_manager.initiate_collaboration(
human_intent, environmental_context, robot_capabilities
)
print(f"Collaboration readiness: {collaboration_setup['collaboration_readiness']}")
print(f"Predicted intentions: {collaboration_setup['predicted_intentions']['primary_intention']}")
print(f"Coordination strategy: {collaboration_setup['coordination_plan']['strategy']}")
print(f"Safety confidence: {collaboration_setup['safety_guarantees']['safety_confidence']:.2f}")
# Simulate collaborative steps
print("\nExecuting collaborative steps...")
for step in range(3):
human_action = {
'type': 'reaching',
'target_object': 'screwdriver',
'position': [0.6, 0.0, 0.1],
'intention': 'grasp_tool'
}
robot_state = {
'position': [0.4, 0.0, 0.1],
'gripper_state': 'open',
'current_task': 'assembly_support'
}
collaboration_context = {
'current_step': step,
'assembly_progress': step / 3,
'last_interaction': time.time() - 5
}
# Execute collaborative step
step_result = collab_manager.execute_collaborative_step(
human_action, robot_state, collaboration_context
)
print(f"Step {step + 1}: Robot response: {step_result['robot_response']['type']}")
print(f" Safety verification: {step_result['safety_verification']}")
print(f" Collaboration progress: {step_result['collaboration_progress']:.2f}")
print("\nCollaboration example completed!")
example_collaborative_interaction()
Evaluation and Validation of Advanced VLA Systems
Comprehensive Evaluation Framework
Evaluating advanced VLA systems for social interaction and cognitive integration requires comprehensive assessment across multiple dimensions.
class AdvancedVLAEvaluator:
"""Comprehensive evaluation framework for advanced VLA systems"""
def __init__(self):
self.social_interaction_evaluator = SocialInteractionEvaluator()
self.cognitive_integration_evaluator = CognitiveIntegrationEvaluator()
self.collaboration_evaluator = CollaborationEvaluator()
self.safety_evaluator = SafetyEvaluator()
self.ethics_evaluator = EthicsEvaluator()
def evaluate_system(self, vla_system, test_scenarios: List[Dict]) -> Dict[str, any]:
"""Evaluate the complete advanced VLA system"""
results = {
'social_interaction': {},
'cognitive_integration': {},
'collaboration_performance': {},
'safety_compliance': {},
'ethical_compliance': {},
'overall_score': 0.0
}
# Evaluate each scenario
for scenario in test_scenarios:
scenario_results = self._evaluate_scenario(vla_system, scenario)
# Aggregate results
for key, value in scenario_results.items():
if key not in results:
results[key] = {}
results[key] = self._aggregate_results(results[key], value)
# Calculate overall score
results['overall_score'] = self._calculate_overall_score(results)
return results
def _evaluate_scenario(self, vla_system, scenario: Dict) -> Dict[str, any]:
"""Evaluate a single scenario"""
scenario_results = {}
# Social interaction evaluation
if 'social_scenario' in scenario:
scenario_results['social_interaction'] = self.social_interaction_evaluator.evaluate(
vla_system, scenario['social_scenario']
)
# Cognitive integration evaluation
if 'cognitive_scenario' in scenario:
scenario_results['cognitive_integration'] = self.cognitive_integration_evaluator.evaluate(
vla_system, scenario['cognitive_scenario']
)
# Collaboration evaluation
if 'collaboration_scenario' in scenario:
scenario_results['collaboration_performance'] = self.collaboration_evaluator.evaluate(
vla_system, scenario['collaboration_scenario']
)
# Safety evaluation
if 'safety_scenario' in scenario:
scenario_results['safety_compliance'] = self.safety_evaluator.evaluate(
vla_system, scenario['safety_scenario']
)
# Ethics evaluation
if 'ethics_scenario' in scenario:
scenario_results['ethical_compliance'] = self.ethics_evaluator.evaluate(
vla_system, scenario['ethics_scenario']
)
return scenario_results
def _aggregate_results(self, existing: Dict, new: Dict) -> Dict:
"""Aggregate evaluation results"""
if not existing:
return new
# Simple aggregation - in practice, this would be more sophisticated
aggregated = existing.copy()
aggregated.update(new)
return aggregated
def _calculate_overall_score(self, results: Dict) -> float:
"""Calculate overall system score"""
weights = {
'social_interaction': 0.25,
'cognitive_integration': 0.25,
'collaboration_performance': 0.25,
'safety_compliance': 0.15,
'ethical_compliance': 0.10
}
total_score = 0.0
total_weight = 0.0
for category, weight in weights.items():
if category in results and 'score' in results[category]:
total_score += results[category]['score'] * weight
total_weight += weight
return total_score / total_weight if total_weight > 0 else 0.0
class SocialInteractionEvaluator:
"""Evaluate social interaction capabilities"""
def __init__(self):
self.metrics = [
'social_awareness',
'context_understanding',
'appropriateness',
'engagement_quality',
'cultural_sensitivity'
]
def evaluate(self, vla_system, scenario: Dict) -> Dict[str, any]:
"""Evaluate social interaction performance"""
# Simulate evaluation process
scores = {}
for metric in self.metrics:
scores[metric] = self._evaluate_metric(vla_system, scenario, metric)
return {
'scores': scores,
'average_score': np.mean(list(scores.values())),
'detailed_feedback': self._generate_feedback(scores),
'score': np.mean(list(scores.values())) # Overall score for this category
}
def _evaluate_metric(self, vla_system, scenario: Dict, metric: str) -> float:
"""Evaluate a specific social interaction metric"""
# Simulate metric evaluation
if metric == 'social_awareness':
return np.random.uniform(0.7, 0.95)
elif metric == 'context_understanding':
return np.random.uniform(0.6, 0.9)
elif metric == 'appropriateness':
return np.random.uniform(0.75, 0.95)
elif metric == 'engagement_quality':
return np.random.uniform(0.7, 0.9)
elif metric == 'cultural_sensitivity':
return np.random.uniform(0.6, 0.85)
else:
return 0.5
def _generate_feedback(self, scores: Dict) -> List[str]:
"""Generate detailed feedback based on scores"""
feedback = []
for metric, score in scores.items():
if score < 0.7:
feedback.append(f"{metric.replace('_', ' ').title()} needs improvement (score: {score:.2f})")
elif score < 0.85:
feedback.append(f"{metric.replace('_', ' ').title()} is adequate (score: {score:.2f})")
else:
feedback.append(f"{metric.replace('_', ' ').title()} is excellent (score: {score:.2f})")
return feedback
class CognitiveIntegrationEvaluator:
"""Evaluate cognitive integration capabilities"""
def __init__(self):
self.cognitive_metrics = [
'memory_integration',
'reasoning_quality',
'planning_effectiveness',
'learning_efficiency',
'adaptation_speed'
]
def evaluate(self, vla_system, scenario: Dict) -> Dict[str, any]:
"""Evaluate cognitive integration performance"""
scores = {}
for metric in self.cognitive_metrics:
scores[metric] = self._evaluate_cognitive_metric(vla_system, scenario, metric)
return {
'scores': scores,
'average_score': np.mean(list(scores.values())),
'cognitive_balance': self._assess_cognitive_balance(scores),
'score': np.mean(list(scores.values()))
}
def _evaluate_cognitive_metric(self, vla_system, scenario: Dict, metric: str) -> float:
"""Evaluate a specific cognitive metric"""
# Simulate cognitive evaluation
cognitive_weights = {
'memory_integration': 0.8,
'reasoning_quality': 0.85,
'planning_effectiveness': 0.8,
'learning_efficiency': 0.75,
'adaptation_speed': 0.7
}
base_score = cognitive_weights.get(metric, 0.7)
return np.random.normal(base_score, 0.1)
def _assess_cognitive_balance(self, scores: Dict) -> str:
"""Assess balance across cognitive functions"""
score_values = list(scores.values())
std_dev = np.std(score_values)
if std_dev < 0.1:
return 'well_balanced'
elif std_dev < 0.2:
return 'moderately_balanced'
else:
return 'imbalanced'
class CollaborationEvaluator:
"""Evaluate collaboration capabilities"""
def __init__(self):
self.collaboration_metrics = [
'coordination_quality',
'task_decomposition',
'communication_effectiveness',
'safety_awareness',
'efficiency_gain'
]
def evaluate(self, vla_system, scenario: Dict) -> Dict[str, any]:
"""Evaluate collaboration performance"""
scores = {}
for metric in self.collaboration_metrics:
scores[metric] = self._evaluate_collaboration_metric(vla_system, scenario, metric)
return {
'scores': scores,
'average_score': np.mean(list(scores.values())),
'collaboration_style': self._identify_collaboration_style(scores),
'score': np.mean(list(scores.values()))
}
def _evaluate_collaboration_metric(self, vla_system, scenario: Dict, metric: str) -> float:
"""Evaluate a specific collaboration metric"""
collaboration_weights = {
'coordination_quality': 0.9,
'task_decomposition': 0.85,
'communication_effectiveness': 0.8,
'safety_awareness': 0.95,
'efficiency_gain': 0.75
}
base_score = collaboration_weights.get(metric, 0.8)
return np.random.normal(base_score, 0.08)
def _identify_collaboration_style(self, scores: Dict) -> str:
"""Identify the dominant collaboration style"""
if scores.get('coordination_quality', 0.5) > 0.8 and scores.get('communication_effectiveness', 0.5) > 0.7:
return 'coordinated_collaboration'
elif scores.get('task_decomposition', 0.5) > 0.8:
return 'task_specialized'
elif scores.get('efficiency_gain', 0.5) > 0.8:
return 'efficiency_focused'
else:
return 'adaptive_collaboration'
class SafetyEvaluator:
"""Evaluate safety compliance"""
def __init__(self):
self.safety_metrics = [
'collision_avoidance',
'force_compliance',
'emergency_response',
'risk_assessment',
'safe_boundary_maintenance'
]
def evaluate(self, vla_system, scenario: Dict) -> Dict[str, any]:
"""Evaluate safety performance"""
scores = {}
for metric in self.safety_metrics:
scores[metric] = self._evaluate_safety_metric(vla_system, scenario, metric)
return {
'scores': scores,
'average_score': np.mean(list(scores.values())),
'safety_critical_areas': self._identify_critical_areas(scores),
'score': np.mean(list(scores.values()))
}
def _evaluate_safety_metric(self, vla_system, scenario: Dict, metric: str) -> float:
"""Evaluate a specific safety metric"""
# Safety is critical - keep scores high
base_score = 0.9
return np.random.normal(base_score, 0.05)
def _identify_critical_areas(self, scores: Dict) -> List[str]:
"""Identify safety areas that need attention"""
critical = []
for metric, score in scores.items():
if score < 0.85:
critical.append(metric)
return critical
class EthicsEvaluator:
"""Evaluate ethical compliance"""
def __init__(self):
self.ethics_metrics = [
'privacy_respect',
'fairness',
'transparency',
'autonomy_support',
'bias_mitigation'
]
def evaluate(self, vla_system, scenario: Dict) -> Dict[str, any]:
"""Evaluate ethical compliance"""
scores = {}
for metric in self.ethics_metrics:
scores[metric] = self._evaluate_ethics_metric(vla_system, scenario, metric)
return {
'scores': scores,
'average_score': np.mean(list(scores.values())),
'ethical_concerns': self._identify_concerns(scores),
'score': np.mean(list(scores.values()))
}
def _evaluate_ethics_metric(self, vla_system, scenario: Dict, metric: str) -> float:
"""Evaluate a specific ethics metric"""
base_score = 0.8
return np.random.normal(base_score, 0.1)
def _identify_concerns(self, scores: Dict) -> List[str]:
"""Identify ethical concerns"""
concerns = []
for metric, score in scores.items():
if score < 0.75:
concerns.append(metric)
return concerns
def example_advanced_evaluation():
"""Example of evaluating advanced VLA system"""
# Create evaluator
evaluator = AdvancedVLAEvaluator()
# Define test scenarios
test_scenarios = [
{
'name': 'social_greeting',
'social_scenario': {
'interaction_type': 'greeting',
'number_of_people': 2,
'cultural_context': 'japanese',
'formality_level': 'high'
},
'cognitive_scenario': {
'memory_task': 'recall_previous_interaction',
'reasoning_task': 'infer_intention',
'planning_task': 'coordinate_response'
},
'collaboration_scenario': {
'task_type': 'object_transfer',
'complexity': 'low',
'safety_level': 'standard'
},
'safety_scenario': {
'environment': 'office',
'obstacles': ['desk', 'chair'],
'people_proximity': 'close'
},
'ethics_scenario': {
'privacy_concerns': 'low',
'fairness_context': 'multi_person',
'transparency_need': 'high'
}
},
{
'name': 'assembly_collaboration',
'social_scenario': {
'interaction_type': 'tool_passing',
'number_of_people': 1,
'cultural_context': 'american',
'formality_level': 'medium'
},
'cognitive_scenario': {
'memory_task': 'remember_tool_sequence',
'reasoning_task': 'predict_next_action',
'planning_task': 'optimize_workspace'
},
'collaboration_scenario': {
'task_type': 'assembly_support',
'complexity': 'high',
'safety_level': 'high'
},
'safety_scenario': {
'environment': 'workshop',
'obstacles': ['tools', 'materials'],
'people_proximity': 'very_close'
},
'ethics_scenario': {
'privacy_concerns': 'medium',
'fairness_context': 'single_person',
'transparency_need': 'medium'
}
}
]
# Create a mock VLA system (in practice, this would be the actual system)
class MockVLASystem:
pass
vla_system = MockVLASystem()
# Run evaluation
results = evaluator.evaluate_system(vla_system, test_scenarios)
print("Advanced VLA System Evaluation Results:")
print("=" * 60)
for category, metrics in results.items():
if category != 'overall_score':
print(f"\n{category.replace('_', ' ').title()} Evaluation:")
print(f" Average Score: {metrics.get('average_score', 0):.3f}")
if 'scores' in metrics:
for metric, score in metrics['scores'].items():
print(f" {metric}: {score:.3f}")
if 'detailed_feedback' in metrics:
print(f" Feedback: {metrics['detailed_feedback'][:2]}...") # Show first 2
print(f"\nOverall System Score: {results['overall_score']:.3f}")
example_advanced_evaluation()
Summary
This chapter has explored the advanced applications of Vision-Language-Action (VLA) models in humanoid robotics, focusing on social interaction capabilities, multi-human scenarios, and cognitive integration. The key topics covered include:
Social Interaction Capabilities:
- Social attention mechanisms for understanding group dynamics
- Social scene understanding and relationship classification
- Social norm compliance and etiquette management
- Cultural adaptation for different social contexts
Multi-Human Scenarios:
- People tracking and interaction prioritization
- Group attention and social scheduling
- Multi-human VLA architectures
- Attention allocation among multiple people
Cognitive Integration:
- Integration with memory, reasoning, and planning systems
- Collaborative human-robot interaction frameworks
- Shared attention mechanisms
- Task decomposition for collaboration
Advanced Behaviors:
- Social norms and etiquette implementation
- Cultural sensitivity and adaptation
- Collaborative task execution
- Safety coordination in human-robot interaction
Evaluation Frameworks:
- Comprehensive evaluation across multiple dimensions
- Social interaction quality assessment
- Cognitive integration effectiveness
- Safety and ethical compliance evaluation
These advanced VLA capabilities enable humanoid robots to engage in natural, intuitive interactions with humans in complex social environments. The integration of social understanding, cognitive reasoning, and collaborative behaviors creates truly intelligent robotic systems that can work effectively alongside humans.
Next Steps
With the completion of Module 4, we've covered all four modules of the Physical AI & Humanoid Robotics textbook:
- Introduction to Physical AI & Humanoid Robotics
- ROS 2 for Humanoid Robotics
- NVIDIA Isaac SDK and Isaac Sim
- Vision-Language-Action Models for Humanoid Robots
The next step would be to integrate all these components into a complete humanoid robot system, implementing the AI-Robot Brain architecture that combines perception, planning, control, and learning systems as discussed throughout these modules.
Estimated Reading Time: 35 minutes