Training VLA Models for Humanoid Robotics
Welcome to Chapter 2 of Module 4: The Interactive AI Brain! This chapter delves into the practical aspects of training Vision-Language-Action (VLA) models specifically for humanoid robotics applications. We'll explore data collection strategies, model architectures optimized for humanoid tasks, training methodologies, and deployment considerations that make VLA models effective for human-robot interaction.
Learning Objectives
By the end of this chapter, you will be able to:
- Design and implement data collection pipelines for humanoid VLA training
- Understand and implement specialized VLA architectures for humanoid tasks
- Apply multi-modal learning techniques for vision-language-action integration
- Implement efficient training strategies for large-scale VLA models
- Evaluate and fine-tune VLA models for specific humanoid platforms
- Deploy trained VLA models on humanoid robot hardware
- Address computational and real-time constraints in humanoid VLA deployment
Data Collection and Annotation for Humanoid VLA
Humanoid-Specific Data Requirements
Training VLA models for humanoid robots requires specialized data that captures the unique aspects of humanoid embodiment, including:
- Multi-view visual data: Head, hand, and body cameras providing different perspectives
- Proprioceptive information: Joint angles, IMU data, and tactile sensors
- Natural language interactions: Commands and conversations in natural settings
- Human demonstration data: Expert demonstrations of humanoid tasks
- Social context data: Interactions with humans in various social settings
import torch
import torch.nn as nn
import numpy as np
from typing import Dict, List, Tuple, Optional, Iterator
from dataclasses import dataclass
import json
import cv2
from PIL import Image
import os
from torch.utils.data import Dataset, DataLoader
import threading
import queue
@dataclass
class HumanoidVLASample:
"""Data structure for humanoid VLA training samples"""
# Visual data
head_camera_image: torch.Tensor # (3, H, W)
left_hand_camera_image: torch.Tensor # (3, H, W)
right_hand_camera_image: torch.Tensor # (3, H, W)
depth_image: Optional[torch.Tensor] = None # (1, H, W)
# Language data
command_text: str
command_tokens: torch.Tensor # (seq_len,)
# Action data
joint_positions: torch.Tensor # (num_joints,)
joint_velocities: Optional[torch.Tensor] = None # (num_joints,)
cartesian_pose: Optional[torch.Tensor] = None # (7,) - position + orientation
# Context data
robot_state: Dict[str, float] # Joint positions, velocities, etc.
environment_state: Dict[str, any] # Object positions, human presence, etc.
task_description: str
success: bool
timestamp: float
class HumanoidDataCollector:
"""System for collecting humanoid VLA training data"""
def __init__(self, robot_interface, data_dir: str = "./humanoid_vla_data"):
self.robot_interface = robot_interface
self.data_dir = data_dir
self.samples = []
self.data_queue = queue.Queue()
self.is_collecting = False
self.collection_thread = None
# Create data directory
os.makedirs(data_dir, exist_ok=True)
os.makedirs(os.path.join(data_dir, "images"), exist_ok=True)
def start_data_collection(self):
"""Start collecting data from the humanoid robot"""
self.is_collecting = True
self.collection_thread = threading.Thread(target=self._collection_loop)
self.collection_thread.start()
def stop_data_collection(self):
"""Stop data collection"""
self.is_collecting = False
if self.collection_thread:
self.collection_thread.join()
def _collection_loop(self):
"""Main data collection loop"""
sample_id = 0
while self.is_collecting:
try:
# Collect multi-modal data
sample = self._collect_single_sample(sample_id)
if sample:
# Save sample to file
self._save_sample(sample, sample_id)
sample_id += 1
# Control collection rate (e.g., 10 Hz)
import time
time.sleep(0.1)
except Exception as e:
print(f"Error in data collection: {e}")
continue
def _collect_single_sample(self, sample_id: int) -> Optional[HumanoidVLASample]:
"""Collect a single VLA sample from the robot"""
try:
# Get visual data from all cameras
head_img = self.robot_interface.get_camera_image('head')
left_hand_img = self.robot_interface.get_camera_image('left_hand')
right_hand_img = self.robot_interface.get_camera_image('right_hand')
depth_img = self.robot_interface.get_depth_image()
# Get robot state
joint_positions = self.robot_interface.get_joint_positions()
joint_velocities = self.robot_interface.get_joint_velocities()
imu_data = self.robot_interface.get_imu_data()
# Get environment state (simplified)
environment_state = {
'object_positions': self.robot_interface.get_object_positions(),
'human_positions': self.robot_interface.get_human_positions(),
'room_layout': self.robot_interface.get_room_layout()
}
# For demonstration, we'll use a placeholder command
# In practice, this would come from human interaction or demonstration
command_text = "move forward" # This would be dynamic
# Create sample
sample = HumanoidVLASample(
head_camera_image=self._preprocess_image(head_img),
left_hand_camera_image=self._preprocess_image(left_hand_img),
right_hand_camera_image=self._preprocess_image(right_hand_img),
depth_image=self._preprocess_depth(depth_img) if depth_img is not None else None,
command_text=command_text,
command_tokens=self._tokenize_command(command_text),
joint_positions=torch.tensor(joint_positions, dtype=torch.float32),
joint_velocities=torch.tensor(joint_velocities, dtype=torch.float32) if joint_velocities is not None else None,
cartesian_pose=self._get_end_effector_pose(), # Simplified
robot_state={
'joint_positions': joint_positions,
'joint_velocities': joint_velocities,
'imu_orientation': imu_data.get('orientation', [0, 0, 0, 1])
},
environment_state=environment_state,
task_description="navigation",
success=True, # Placeholder
timestamp=time.time()
)
return sample
except Exception as e:
print(f"Error collecting sample {sample_id}: {e}")
return None
def _preprocess_image(self, img) -> torch.Tensor:
"""Preprocess camera image for training"""
# Convert to tensor and normalize
if isinstance(img, np.ndarray):
img = Image.fromarray(img)
# Resize and convert to tensor
img = img.resize((224, 224)) # Standard size for vision models
img_tensor = torch.from_numpy(np.array(img)).permute(2, 0, 1).float() / 255.0
return img_tensor
def _preprocess_depth(self, depth_img) -> torch.Tensor:
"""Preprocess depth image"""
if isinstance(depth_img, np.ndarray):
depth_tensor = torch.from_numpy(depth_img).unsqueeze(0).float()
else:
depth_tensor = depth_img.unsqueeze(0) if isinstance(depth_img, torch.Tensor) else depth_img
return depth_tensor
def _tokenize_command(self, command: str) -> torch.Tensor:
"""Tokenize command text"""
# Simple tokenization for demonstration
vocab = {
'move': 1, 'forward': 2, 'backward': 3, 'left': 4, 'right': 5,
'pick': 6, 'up': 7, 'place': 8, 'down': 9, 'go': 10,
'to': 11, 'the': 12, 'a': 13, 'an': 14, 'object': 15,
'cup': 16, 'box': 17, 'table': 18, 'chair': 19, 'kitchen': 20
}
tokens = []
for word in command.lower().split():
clean_word = ''.join(c for c in word if c.isalnum())
tokens.append(vocab.get(clean_word, 0)) # 0 for unknown words
# Pad to fixed length
tokens = tokens[:20] + [0] * max(0, 20 - len(tokens))
return torch.tensor(tokens, dtype=torch.long)
def _get_end_effector_pose(self) -> torch.Tensor:
"""Get end effector pose (simplified)"""
# This would compute actual pose from kinematics
return torch.zeros(7, dtype=torch.float32) # [x, y, z, qw, qx, qy, qz]
def _save_sample(self, sample: HumanoidVLASample, sample_id: int):
"""Save sample to file"""
# Save images
img_dir = os.path.join(self.data_dir, "images")
# Convert tensors back to images for saving
def tensor_to_pil(tensor):
# Denormalize and convert back to PIL
img_array = (tensor.permute(1, 2, 0).numpy() * 255).astype(np.uint8)
return Image.fromarray(img_array)
head_img_path = os.path.join(img_dir, f"head_{sample_id:06d}.png")
left_img_path = os.path.join(img_dir, f"left_{sample_id:06d}.png")
right_img_path = os.path.join(img_dir, f"right_{sample_id:06d}.png")
tensor_to_pil(sample.head_camera_image).save(head_img_path)
tensor_to_pil(sample.left_hand_camera_image).save(left_img_path)
tensor_to_pil(sample.right_hand_camera_image).save(right_img_path)
# Save metadata as JSON
metadata = {
'sample_id': sample_id,
'command_text': sample.command_text,
'command_tokens': sample.command_tokens.tolist(),
'joint_positions': sample.joint_positions.tolist(),
'joint_velocities': sample.joint_velocities.tolist() if sample.joint_velocities is not None else None,
'robot_state': sample.robot_state,
'environment_state': sample.environment_state,
'task_description': sample.task_description,
'success': sample.success,
'timestamp': sample.timestamp
}
metadata_path = os.path.join(self.data_dir, f"metadata_{sample_id:06d}.json")
with open(metadata_path, 'w') as f:
json.dump(metadata, f, indent=2)
class HumanoidVLADataset(Dataset):
"""Dataset for humanoid VLA training"""
def __init__(self, data_dir: str, max_samples: Optional[int] = None):
self.data_dir = data_dir
self.metadata_files = self._find_metadata_files()
if max_samples:
self.metadata_files = self.metadata_files[:max_samples]
def _find_metadata_files(self) -> List[str]:
"""Find all metadata files in the data directory"""
metadata_files = []
for file in os.listdir(self.data_dir):
if file.startswith('metadata_') and file.endswith('.json'):
metadata_files.append(os.path.join(self.data_dir, file))
return sorted(metadata_files)
def __len__(self) -> int:
return len(self.metadata_files)
def __getitem__(self, idx: int) -> Dict[str, torch.Tensor]:
"""Get a single sample from the dataset"""
# Load metadata
with open(self.metadata_files[idx], 'r') as f:
metadata = json.load(f)
# Load images
sample_id = metadata['sample_id']
img_dir = os.path.join(self.data_dir, "images")
head_img_path = os.path.join(img_dir, f"head_{sample_id:06d}.png")
left_img_path = os.path.join(img_dir, f"left_{sample_id:06d}.png")
right_img_path = os.path.join(img_dir, f"right_{sample_id:06d}.png")
# Load and preprocess images
head_img = self._load_and_preprocess_image(head_img_path)
left_img = self._load_and_preprocess_image(left_img_path)
right_img = self._load_and_preprocess_image(right_img_path)
# Combine visual data
visual_data = torch.stack([head_img, left_img, right_img], dim=0) # (3, C, H, W)
# Language data
command_tokens = torch.tensor(metadata['command_tokens'], dtype=torch.long)
# Action data
joint_positions = torch.tensor(metadata['joint_positions'], dtype=torch.float32)
return {
'visual_data': visual_data,
'language_data': command_tokens,
'action_data': joint_positions,
'robot_state': torch.tensor(list(metadata['robot_state']['joint_positions']), dtype=torch.float32),
'environment_state': metadata['environment_state'],
'success': metadata['success']
}
def _load_and_preprocess_image(self, img_path: str) -> torch.Tensor:
"""Load and preprocess a single image"""
img = Image.open(img_path).convert('RGB')
img = img.resize((224, 224))
img_tensor = torch.from_numpy(np.array(img)).permute(2, 0, 1).float() / 255.0
return img_tensor
# Example usage of data collection and dataset
def example_data_collection():
"""Example of data collection and dataset usage"""
# This is a simulation - in practice, you would connect to a real robot
class MockRobotInterface:
def get_camera_image(self, camera_name: str):
return np.random.randint(0, 255, (480, 640, 3), dtype=np.uint8)
def get_depth_image(self):
return np.random.rand(480, 640).astype(np.float32)
def get_joint_positions(self):
return [0.1 * i for i in range(28)] # 28 joints example
def get_joint_velocities(self):
return [0.01 * i for i in range(28)]
def get_imu_data(self):
return {'orientation': [1, 0, 0, 0]}
def get_object_positions(self):
return {'cup': [1.0, 0.5, 0.8]}
def get_human_positions(self):
return {'person1': [0.5, 0.0, 0.0]}
def get_room_layout(self):
return {'table': [1.0, 0.0, 0.0], 'chair': [0.0, 1.0, 0.0]}
# Create data collector
robot_interface = MockRobotInterface()
collector = HumanoidDataCollector(robot_interface, "./mock_vla_data")
print("Starting data collection simulation...")
# Collect a few samples (in practice, run for longer)
import time
collector.start_data_collection()
time.sleep(2) # Collect for 2 seconds
collector.stop_data_collection()
print("Data collection completed.")
# Create dataset
if os.path.exists("./mock_vla_data"):
dataset = HumanoidVLADataset("./mock_vla_data", max_samples=5)
print(f"Dataset created with {len(dataset)} samples")
# Test data loading
if len(dataset) > 0:
sample = dataset[0]
print(f"Sample visual data shape: {sample['visual_data'].shape}")
print(f"Sample language data shape: {sample['language_data'].shape}")
print(f"Sample action data shape: {sample['action_data'].shape}")
# Clean up
import shutil
if os.path.exists("./mock_vla_data"):
shutil.rmtree("./mock_vla_data")
example_data_collection()
Demonstration Learning for Humanoid VLA
Demonstration learning is crucial for humanoid robots, as it allows them to learn from human experts performing tasks in natural ways.
class DemonstrationLearningCollector:
"""Collect demonstration data for humanoid VLA models"""
def __init__(self, robot_interface):
self.robot_interface = robot_interface
self.demonstrations = []
self.current_demonstration = []
self.is_recording = False
def start_recording_demonstration(self, task_description: str):
"""Start recording a new demonstration"""
self.current_demonstration = []
self.is_recording = True
self.task_description = task_description
print(f"Started recording demonstration for: {task_description}")
def stop_recording_demonstration(self) -> List[Dict]:
"""Stop recording and return the demonstration"""
self.is_recording = False
demo = self.current_demonstration.copy()
self.demonstrations.append({
'demonstration': demo,
'task_description': self.task_description,
'timestamp': time.time()
})
self.current_demonstration = []
print(f"Stopped recording. Collected {len(demo)} steps.")
return demo
def record_step(self, command: str = None):
"""Record a single step in the demonstration"""
if not self.is_recording:
return
try:
# Collect current state
step_data = {
'timestamp': time.time(),
'visual_data': {
'head': self.robot_interface.get_camera_image('head'),
'left_hand': self.robot_interface.get_camera_image('left_hand'),
'right_hand': self.robot_interface.get_camera_image('right_hand')
},
'robot_state': {
'joint_positions': self.robot_interface.get_joint_positions(),
'joint_velocities': self.robot_interface.get_joint_velocities(),
'end_effector_pose': self.robot_interface.get_end_effector_pose()
},
'command': command,
'action_taken': self.robot_interface.get_current_action(), # What the demonstrator did
'environment_state': self.robot_interface.get_environment_state()
}
self.current_demonstration.append(step_data)
except Exception as e:
print(f"Error recording step: {e}")
def augment_demonstration(self, demonstration: List[Dict]) -> List[Dict]:
"""Augment demonstration with additional information and variations"""
augmented_demo = []
for step in demonstration:
# Add augmented versions of the step
augmented_steps = self._augment_single_step(step)
augmented_demo.extend(augmented_steps)
return augmented_demo
def _augment_single_step(self, step: Dict) -> List[Dict]:
"""Augment a single step with variations"""
augmented_steps = [step] # Original step
# Add visual augmentations
for aug_type in ['rotation', 'scaling', 'color_jitter']:
augmented_step = step.copy()
augmented_step['visual_data'] = self._apply_visual_augmentation(
step['visual_data'], aug_type
)
augmented_steps.append(augmented_step)
# Add language variations (paraphrasing)
if step['command']:
paraphrases = self._generate_paraphrases(step['command'])
for para in paraphrases:
augmented_step = step.copy()
augmented_step['command'] = para
augmented_steps.append(augmented_step)
return augmented_steps
def _apply_visual_augmentation(self, visual_data: Dict, aug_type: str) -> Dict:
"""Apply visual augmentation to images"""
augmented_data = visual_data.copy()
for camera_name, img in visual_data.items():
if aug_type == 'rotation':
# Apply random rotation
angle = np.random.uniform(-10, 10) # -10 to 10 degrees
augmented_data[camera_name] = self._rotate_image(img, angle)
elif aug_type == 'scaling':
# Apply random scaling
scale = np.random.uniform(0.9, 1.1) # 90% to 110% scale
augmented_data[camera_name] = self._scale_image(img, scale)
elif aug_type == 'color_jitter':
# Apply color jittering
augmented_data[camera_name] = self._jitter_colors(img)
return augmented_data
def _rotate_image(self, img, angle):
"""Rotate image by given angle"""
# This is a simplified implementation
# In practice, use OpenCV or PIL for proper rotation
return img
def _scale_image(self, img, scale):
"""Scale image by given factor"""
# This is a simplified implementation
return img
def _jitter_colors(self, img):
"""Apply color jittering"""
# This is a simplified implementation
return img
def _generate_paraphrases(self, command: str) -> List[str]:
"""Generate paraphrases of a command"""
# Simple paraphrase generation for demonstration
paraphrases = []
# Example paraphrases for common commands
command_lower = command.lower()
if 'pick up' in command_lower:
paraphrases.append(command.replace('pick up', 'grasp'))
paraphrases.append(command.replace('pick up', 'take'))
elif 'move to' in command_lower:
paraphrases.append(command.replace('move to', 'go to'))
paraphrases.append(command.replace('move to', 'navigate to'))
return paraphrases
class HumanoidDemoDataset(Dataset):
"""Dataset for demonstration-based humanoid VLA training"""
def __init__(self, demonstrations: List[Dict], transform=None):
self.demonstrations = demonstrations
self.transform = transform
self.flattened_steps = []
# Flatten all demonstration steps
for demo in demonstrations:
for step in demo['demonstration']:
self.flattened_steps.append({
'demo_data': step,
'task': demo['task_description']
})
def __len__(self):
return len(self.flattened_steps)
def __getitem__(self, idx):
step_data = self.flattened_steps[idx]['demo_data']
task = self.flattened_steps[idx]['task']
# Process visual data
visual_tensors = []
for cam_name, img in step_data['visual_data'].items():
if isinstance(img, np.ndarray):
img_tensor = torch.from_numpy(img).permute(2, 0, 1).float() / 255.0
else:
img_tensor = img
visual_tensors.append(img_tensor)
visual_data = torch.stack(visual_tensors, dim=0)
# Process language data
command_tokens = self._tokenize_command(step_data['command'])
# Process action data
action_data = torch.tensor(step_data['action_taken'], dtype=torch.float32)
return {
'visual_data': visual_data,
'language_data': command_tokens,
'action_data': action_data,
'robot_state': torch.tensor(step_data['robot_state']['joint_positions'], dtype=torch.float32),
'task_type': task
}
def _tokenize_command(self, command: str) -> torch.Tensor:
"""Tokenize command text"""
# Reuse tokenization logic from earlier
vocab = {
'move': 1, 'forward': 2, 'backward': 3, 'left': 4, 'right': 5,
'pick': 6, 'up': 7, 'place': 8, 'down': 9, 'go': 10,
'to': 11, 'the': 12, 'a': 13, 'an': 14, 'object': 15,
'cup': 16, 'box': 17, 'table': 18, 'chair': 19, 'kitchen': 20
}
tokens = []
for word in command.lower().split():
clean_word = ''.join(c for c in word if c.isalnum())
tokens.append(vocab.get(clean_word, 0))
# Pad to fixed length
tokens = tokens[:20] + [0] * max(0, 20 - len(tokens))
return torch.tensor(tokens, dtype=torch.long)
VLA Model Architectures for Humanoid Applications
Multi-View Vision Processing
Humanoid robots have multiple cameras providing different perspectives, which requires specialized vision processing architectures.
class MultiViewVisionEncoder(nn.Module):
"""Vision encoder for multi-view humanoid camera setup"""
def __init__(self, num_cameras: int = 3, hidden_dim: int = 512):
super().__init__()
self.num_cameras = num_cameras
self.hidden_dim = hidden_dim
# Separate encoders for each camera view
self.camera_encoders = nn.ModuleList([
self._create_single_camera_encoder() for _ in range(num_cameras)
])
# Cross-view attention to integrate information from different views
self.cross_view_attention = nn.MultiheadAttention(
embed_dim=hidden_dim,
num_heads=8,
batch_first=True
)
# Fusion layer to combine multi-view features
self.fusion_layer = nn.Sequential(
nn.Linear(hidden_dim * num_cameras, hidden_dim * 2),
nn.ReLU(),
nn.Linear(hidden_dim * 2, hidden_dim),
nn.LayerNorm(hidden_dim)
)
# View-specific positional encoding
self.view_positional_encoding = nn.Parameter(
torch.randn(num_cameras, hidden_dim)
)
def _create_single_camera_encoder(self) -> nn.Module:
"""Create encoder for a single camera view"""
return nn.Sequential(
# CNN backbone for feature extraction
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.MaxPool2d(kernel_size=3, stride=2, padding=1),
nn.Conv2d(128, 256, kernel_size=3, padding=1),
nn.BatchNorm2d(256),
nn.ReLU(),
# Global average pooling
nn.AdaptiveAvgPool2d((1, 1)),
# Projection to hidden dimension
nn.Flatten(),
nn.Linear(256, self.hidden_dim),
nn.ReLU()
)
def forward(self, multi_view_images: torch.Tensor) -> torch.Tensor:
"""
Forward pass through multi-view vision encoder
Args:
multi_view_images: (batch_size, num_cameras, channels, height, width)
Returns:
fused_features: (batch_size, hidden_dim)
"""
batch_size = multi_view_images.size(0)
camera_features = []
# Process each camera view separately
for i in range(self.num_cameras):
view_images = multi_view_images[:, i] # (batch, C, H, W)
view_features = self.camera_encoders[i](view_images) # (batch, hidden_dim)
camera_features.append(view_features)
# Stack features: (batch, num_cameras, hidden_dim)
stacked_features = torch.stack(camera_features, dim=1)
# Add view-specific positional encoding
positional_encoding = self.view_positional_encoding.unsqueeze(0).expand(batch_size, -1, -1)
encoded_features = stacked_features + positional_encoding
# Apply cross-view attention
attended_features, attention_weights = self.cross_view_attention(
encoded_features, encoded_features, encoded_features
)
# Residual connection
attended_features = attended_features + stacked_features
# Flatten and fuse features
flattened = attended_features.view(batch_size, -1) # (batch, num_cameras * hidden_dim)
fused_features = self.fusion_layer(flattened)
return fused_features
class HumanoidVLAModel(nn.Module):
"""Complete VLA model for humanoid robotics"""
def __init__(self,
num_cameras: int = 3,
vocab_size: int = 10000,
hidden_dim: int = 512,
action_dim: int = 28, # Example: 28 humanoid joints
num_heads: int = 8,
num_layers: int = 6):
super().__init__()
# Multi-view vision encoder
self.vision_encoder = MultiViewVisionEncoder(
num_cameras=num_cameras,
hidden_dim=hidden_dim
)
# Language encoder
self.language_encoder = nn.TransformerEncoder(
nn.TransformerEncoderLayer(
d_model=hidden_dim,
nhead=num_heads,
dim_feedforward=hidden_dim * 4,
batch_first=True
),
num_layers=num_layers
)
self.text_embedding = nn.Embedding(vocab_size, hidden_dim)
self.text_pos_encoding = nn.Parameter(torch.randn(50, hidden_dim))
# Proprioceptive encoder for joint states
self.proprio_encoder = nn.Sequential(
nn.Linear(28 * 2, hidden_dim), # joint positions + velocities
nn.ReLU(),
nn.Linear(hidden_dim, hidden_dim)
)
# Multimodal fusion transformer
self.fusion_transformer = nn.TransformerEncoder(
nn.TransformerEncoderLayer(
d_model=hidden_dim,
nhead=num_heads,
dim_feedforward=hidden_dim * 4,
batch_first=True,
dropout=0.1
),
num_layers=num_layers
)
# Action decoder
self.action_decoder = nn.Sequential(
nn.LayerNorm(hidden_dim),
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),
nn.Tanh() # Actions in [-1, 1] range
)
# Task-specific adaptation layers
self.task_adapters = nn.ModuleDict()
self.dropout = nn.Dropout(0.1)
def forward(self,
multi_view_images: torch.Tensor,
text_tokens: torch.Tensor,
proprio_state: torch.Tensor,
task_id: Optional[str] = None) -> torch.Tensor:
"""
Forward pass through the humanoid VLA model
Args:
multi_view_images: (batch, num_cameras, 3, H, W)
text_tokens: (batch, seq_len)
proprio_state: (batch, joint_dim * 2) - positions and velocities
task_id: Optional task identifier for adaptation
Returns:
actions: (batch, action_dim)
"""
batch_size = multi_view_images.size(0)
# Encode vision
vision_features = self.vision_encoder(multi_view_images) # (batch, hidden_dim)
# Encode language
text_embedded = self.text_embedding(text_tokens) # (batch, seq_len, hidden_dim)
seq_len = text_embedded.size(1)
pos_encoding = self.text_pos_encoding[:seq_len].unsqueeze(0).expand(batch_size, -1, -1)
text_features = text_embedded + pos_encoding
text_encoded = self.language_encoder(text_features) # (batch, seq_len, hidden_dim)
# Use mean pooling for single representation
text_features = text_encoded.mean(dim=1) # (batch, hidden_dim)
# Encode proprioceptive state
proprio_features = self.proprio_encoder(proprio_state) # (batch, hidden_dim)
# Combine all modalities
combined_features = torch.stack([
vision_features, # Vision features
text_features, # Language features
proprio_features # Proprioceptive features
], dim=1) # (batch, 3, hidden_dim)
# Apply fusion transformer
fused_features = self.fusion_transformer(combined_features)
# Use the first token (or mean) as the integrated representation
integrated_features = fused_features.mean(dim=1) # (batch, hidden_dim)
# Apply task-specific adaptation if provided
if task_id and task_id in self.task_adapters:
integrated_features = self.task_adapters[task_id](integrated_features)
# Add dropout
integrated_features = self.dropout(integrated_features)
# Decode to actions
actions = self.action_decoder(integrated_features)
return actions
def add_task_adapter(self, task_id: str, bottleneck_dim: int = 128):
"""Add a task-specific adapter for fine-tuning"""
adapter = nn.Sequential(
nn.Linear(self.hidden_dim, bottleneck_dim),
nn.ReLU(),
nn.Linear(bottleneck_dim, self.hidden_dim),
nn.LayerNorm(self.hidden_dim)
)
self.task_adapters[task_id] = adapter
# Example usage of the complete model
def example_humanoid_vla_model():
"""Example usage of the humanoid VLA model"""
# Create model
model = HumanoidVLAModel(
num_cameras=3,
vocab_size=10000,
hidden_dim=512,
action_dim=28, # 28 joints for example humanoid
num_heads=8,
num_layers=6
)
# Create sample inputs
batch_size = 2
multi_view_images = torch.randn(batch_size, 3, 3, 224, 224) # 3 cameras, RGB, 224x224
text_tokens = torch.randint(0, 10000, (batch_size, 10)) # 10 tokens per sequence
proprio_state = torch.randn(batch_size, 28 * 2) # 28 joints * 2 (pos + vel)
# Forward pass
actions = model(multi_view_images, text_tokens, proprio_state)
print(f"Input shapes:")
print(f" Multi-view images: {multi_view_images.shape}")
print(f" Text tokens: {text_tokens.shape}")
print(f" Proprioceptive state: {proprio_state.shape}")
print(f"Output actions shape: {actions.shape}")
print(f"Action range: [{actions.min():.3f}, {actions.max():.3f}]")
example_humanoid_vla_model()
Hierarchical Action Spaces
Humanoid robots operate at multiple levels of abstraction, requiring hierarchical action spaces that can handle both high-level goals and low-level motor commands.
class HierarchicalActionSpace:
"""Hierarchical action space for humanoid robots"""
def __init__(self, action_space_config: Dict):
self.action_space_config = action_space_config
self.hierarchy_levels = self._build_hierarchy()
def _build_hierarchy(self) -> Dict:
"""Build the hierarchical action space"""
return {
'high_level': {
'type': 'discrete',
'actions': ['navigate', 'manipulate', 'communicate', 'wait'],
'dimension': 4
},
'mid_level': {
'navigate': {
'type': 'continuous',
'dimension': 3, # x, y, theta
'range': [(-1, 1), (-1, 1), (-1, 1)]
},
'manipulate': {
'type': 'continuous',
'dimension': 7, # end-effector pose (position + orientation)
'range': [(-1, 1)] * 7
},
'communicate': {
'type': 'discrete',
'actions': ['speak', 'gesture', 'express_emotion'],
'dimension': 3
}
},
'low_level': {
'type': 'continuous',
'dimension': 28, # joint positions for example humanoid
'range': [(-2.5, 2.5)] * 28 # joint limits in radians
}
}
def discretize_action(self, continuous_action: torch.Tensor, level: str) -> torch.Tensor:
"""Discretize continuous action for specific level"""
if level == 'high_level':
# Map continuous values to discrete actions
action_idx = torch.argmax(continuous_action, dim=-1)
return action_idx
else:
return continuous_action # Keep continuous for other levels
class HierarchicalVLA(nn.Module):
"""Hierarchical VLA model with multiple action levels"""
def __init__(self,
num_cameras: int = 3,
vocab_size: int = 10000,
hidden_dim: int = 512,
action_hierarchy: Optional[HierarchicalActionSpace] = None):
super().__init__()
self.action_hierarchy = action_hierarchy or HierarchicalActionSpace({})
# Shared backbone for multimodal processing
self.shared_encoder = HumanoidVLAModel(
num_cameras=num_cameras,
vocab_size=vocab_size,
hidden_dim=hidden_dim,
action_dim=hidden_dim # Output shared representation
)
# Separate decoders for each hierarchy level
self.high_level_decoder = nn.Sequential(
nn.Linear(hidden_dim, 256),
nn.ReLU(),
nn.Linear(256, self.action_hierarchy.hierarchy_levels['high_level']['dimension'])
)
self.mid_level_decoders = nn.ModuleDict()
for action_type, config in self.action_hierarchy.hierarchy_levels['mid_level'].items():
self.mid_level_decoders[action_type] = nn.Sequential(
nn.Linear(hidden_dim, 256),
nn.ReLU(),
nn.Linear(256, config['dimension'])
)
self.low_level_decoder = nn.Sequential(
nn.Linear(hidden_dim, 512),
nn.ReLU(),
nn.Linear(512, 28) # 28 joints
)
# Hierarchy selector
self.hierarchy_selector = nn.Linear(hidden_dim, 3) # high, mid, low level selection
def forward(self,
multi_view_images: torch.Tensor,
text_tokens: torch.Tensor,
proprio_state: torch.Tensor,
hierarchy_level: str = 'low') -> Dict[str, torch.Tensor]:
"""
Forward pass with hierarchical action selection
Args:
multi_view_images: Multi-view camera images
text_tokens: Language command tokens
proprio_state: Current robot state
hierarchy_level: Which level to output ('high', 'mid', 'low', or 'auto')
Returns:
Dictionary with actions at specified level
"""
# Get shared representation
shared_repr = self.shared_encoder(multi_view_images, text_tokens, proprio_state)
if hierarchy_level == 'auto':
# Automatically select hierarchy level
level_probs = torch.softmax(self.hierarchy_selector(shared_repr), dim=-1)
selected_level_idx = torch.argmax(level_probs, dim=-1)
level_names = ['high', 'mid', 'low']
hierarchy_level = level_names[selected_level_idx[0].item()]
results = {'selected_level': hierarchy_level}
if hierarchy_level == 'high':
high_actions = self.high_level_decoder(shared_repr)
results['actions'] = high_actions
results['action_type'] = 'high_level'
elif hierarchy_level == 'mid':
# Select mid-level action type based on high-level decision
high_actions = self.high_level_decoder(shared_repr)
high_action_type = torch.argmax(high_actions, dim=-1)
# For simplicity, use the first valid action type
action_types = list(self.mid_level_decoders.keys())
mid_action_type = action_types[high_action_type[0].item() % len(action_types)]
mid_actions = self.mid_level_decoders[mid_action_type](shared_repr)
results['actions'] = mid_actions
results['action_type'] = f'mid_level_{mid_action_type}'
else: # low level
low_actions = self.low_level_decoder(shared_repr)
results['actions'] = low_actions
results['action_type'] = 'low_level_joints'
return results
def example_hierarchical_vla():
"""Example of hierarchical VLA model"""
# Create hierarchical VLA model
hierarchy = HierarchicalActionSpace({})
model = HierarchicalVLA(action_hierarchy=hierarchy)
# 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)
# Test different hierarchy levels
for level in ['high', 'mid', 'low', 'auto']:
results = model(multi_view_images, text_tokens, proprio_state, hierarchy_level=level)
print(f"{level.upper()} level - Action type: {results['action_type']}, "
f"Action shape: {results['actions'].shape}")
example_hierarchical_vla()
Training Strategies for Humanoid VLA
Multi-Task Learning Framework
Training VLA models for humanoid robots benefits from multi-task learning, where the model learns to perform multiple related tasks simultaneously.
class MultiTaskVLATrainer:
"""Multi-task training framework for humanoid VLA models"""
def __init__(self,
model: nn.Module,
tasks: List[str],
task_weights: Optional[Dict[str, float]] = None,
learning_rate: float = 1e-4):
self.model = model
self.tasks = tasks
self.task_weights = task_weights or {task: 1.0 for task in tasks}
# Separate optimizers for different components if needed
self.optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
self.scaler = torch.cuda.amp.GradScaler() if torch.cuda.is_available() else None
# Task-specific loss functions
self.criterion = nn.MSELoss()
self.task_criterions = {task: nn.MSELoss() for task in tasks}
# Performance tracking
self.task_losses = {task: [] for task in tasks}
self.total_losses = []
def train_step(self, batch: Dict, task_type: str) -> float:
"""Single training step for a specific task"""
self.model.train()
# Extract batch data
multi_view_images = batch['visual_data'] # (batch, num_cameras, C, H, W)
text_tokens = batch['language_data'] # (batch, seq_len)
target_actions = batch['action_data'] # (batch, action_dim)
proprio_state = batch['robot_state'] # (batch, state_dim)
if self.scaler is not None:
# Mixed precision training
with torch.cuda.amp.autocast():
outputs = self.model(multi_view_images, text_tokens, proprio_state)
if isinstance(outputs, dict) and 'actions' in outputs:
predicted_actions = outputs['actions']
else:
predicted_actions = outputs
loss = self.task_criterions[task_type](predicted_actions, target_actions)
weighted_loss = loss * self.task_weights.get(task_type, 1.0)
self.optimizer.zero_grad()
self.scaler.scale(weighted_loss).backward()
self.scaler.step(self.optimizer)
self.scaler.update()
else:
# Standard training
outputs = self.model(multi_view_images, text_tokens, proprio_state)
if isinstance(outputs, dict) and 'actions' in outputs:
predicted_actions = outputs['actions']
else:
predicted_actions = outputs
loss = self.task_criterions[task_type](predicted_actions, target_actions)
weighted_loss = loss * self.task_weights.get(task_type, 1.0)
self.optimizer.zero_grad()
weighted_loss.backward()
self.optimizer.step()
# Track losses
self.task_losses[task_type].append(loss.item())
self.total_losses.append(weighted_loss.item())
return weighted_loss.item()
def train_epoch(self, data_loaders: Dict[str, DataLoader], num_batches: int = 100) -> Dict[str, float]:
"""Train for one epoch with multiple tasks"""
epoch_losses = {task: 0.0 for task in self.tasks}
batch_counts = {task: 0 for task in self.tasks}
# Cycle through tasks
task_iterators = {task: iter(dataloader) for task, dataloader in data_loaders.items()}
for batch_idx in range(num_batches):
# Select task (round-robin or weighted random)
current_task = self.tasks[batch_idx % len(self.tasks)]
try:
batch = next(task_iterators[current_task])
loss = self.train_step(batch, current_task)
epoch_losses[current_task] += loss
batch_counts[current_task] += 1
except StopIteration:
# Reset iterator if dataset is exhausted
task_iterators[current_task] = iter(data_loaders[current_task])
batch = next(task_iterators[current_task])
loss = self.train_step(batch, current_task)
epoch_losses[current_task] += loss
batch_counts[current_task] += 1
# Calculate average losses per task
avg_losses = {
task: epoch_losses[task] / batch_counts[task] if batch_counts[task] > 0 else 0.0
for task in self.tasks
}
return avg_losses
def evaluate(self, data_loaders: Dict[str, DataLoader]) -> Dict[str, Dict[str, float]]:
"""Evaluate model on multiple tasks"""
self.model.eval()
results = {}
with torch.no_grad():
for task, dataloader in data_loaders.items():
task_losses = []
task_accuracies = []
for batch in dataloader:
multi_view_images = batch['visual_data']
text_tokens = batch['language_data']
target_actions = batch['action_data']
proprio_state = batch['robot_state']
outputs = self.model(multi_view_images, text_tokens, proprio_state)
if isinstance(outputs, dict) and 'actions' in outputs:
predicted_actions = outputs['actions']
else:
predicted_actions = outputs
# Calculate loss
loss = self.task_criterions[task](predicted_actions, target_actions)
task_losses.append(loss.item())
# Calculate accuracy (simplified)
action_similarity = torch.cosine_similarity(
predicted_actions, target_actions, dim=1
).mean().item()
task_accuracies.append(action_similarity)
results[task] = {
'avg_loss': np.mean(task_losses),
'avg_accuracy': np.mean(task_accuracies),
'num_samples': len(task_losses)
}
return results
class CurriculumLearningScheduler:
"""Curriculum learning for VLA training"""
def __init__(self, tasks: List[str], difficulty_levels: Dict[str, List[str]]):
self.tasks = tasks
self.difficulty_levels = difficulty_levels
self.current_level = 0
self.level_progress = {task: 0.0 for task in tasks}
def get_current_tasks(self) -> List[str]:
"""Get tasks appropriate for current difficulty level"""
if self.current_level < len(self.difficulty_levels):
return list(self.difficulty_levels.keys())[:self.current_level + 1]
else:
return self.tasks
def update_level(self, performance_metrics: Dict[str, float]):
"""Update curriculum level based on performance"""
avg_performance = np.mean(list(performance_metrics.values()))
# Move to next level if performance is good enough
if avg_performance > 0.8 and self.current_level < len(self.difficulty_levels) - 1:
self.current_level += 1
print(f"Advancing to curriculum level {self.current_level + 1}")
def example_multi_task_training():
"""Example of multi-task VLA training"""
# Create model
model = HumanoidVLAModel(action_dim=28)
# Define tasks
tasks = ['navigation', 'manipulation', 'social_interaction']
# Create trainer
trainer = MultiTaskVLATrainer(model, tasks)
# Simulate data loaders for each task (in practice, these would be real datasets)
class MockTaskDataset(Dataset):
def __init__(self, size=100):
self.size = size
def __len__(self):
return self.size
def __getitem__(self, idx):
return {
'visual_data': torch.randn(2, 3, 3, 224, 224), # batch of 2
'language_data': torch.randint(0, 10000, (2, 10)),
'action_data': torch.randn(2, 28),
'robot_state': torch.randn(2, 28 * 2)
}
# Create mock data loaders
data_loaders = {
task: DataLoader(MockTaskDataset(50), batch_size=2, shuffle=True)
for task in tasks
}
print("Starting multi-task training...")
# Train for a few epochs
for epoch in range(3):
epoch_losses = trainer.train_epoch(data_loaders, num_batches=30)
print(f"Epoch {epoch + 1} losses: {epoch_losses}")
# Evaluate
eval_results = trainer.evaluate(data_loaders)
print(f"Evaluation results: {eval_results}")
example_multi_task_training()
Imitation Learning and Reinforcement Learning Integration
Combining imitation learning from demonstrations with reinforcement learning can create more robust VLA models.
class ImitationReinforcementTrainer:
"""Training framework combining imitation and reinforcement learning"""
def __init__(self,
model: nn.Module,
learning_rate: float = 1e-4,
imitation_weight: float = 0.7,
reinforcement_weight: float = 0.3,
entropy_weight: float = 0.01):
self.model = model
self.imitation_weight = imitation_weight
self.reinforcement_weight = reinforcement_weight
self.entropy_weight = entropy_weight
self.optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
self.imitation_criterion = nn.MSELoss()
# For RL component
self.value_network = nn.Sequential(
nn.Linear(512, 256),
nn.ReLU(),
nn.Linear(256, 1)
)
self.value_optimizer = torch.optim.AdamW(self.value_network.parameters(), lr=learning_rate)
def imitation_loss(self,
multi_view_images: torch.Tensor,
text_tokens: torch.Tensor,
proprio_state: torch.Tensor,
target_actions: torch.Tensor) -> torch.Tensor:
"""Calculate imitation learning loss"""
predicted_actions = self.model(multi_view_images, text_tokens, proprio_state)
return self.imitation_criterion(predicted_actions, target_actions)
def compute_advantages(self, rewards: List[float], values: List[float], gamma: float = 0.99) -> List[float]:
"""Compute advantages using Generalized Advantage Estimation (GAE)"""
advantages = []
gae = 0.0
for i in reversed(range(len(rewards))):
if i == len(rewards) - 1:
next_value = 0.0
else:
next_value = values[i + 1]
delta = rewards[i] + gamma * next_value - values[i]
gae = delta + gamma * 0.95 * gae # Lambda = 0.95 for GAE
advantages.insert(0, gae)
return advantages
def reinforcement_loss(self,
multi_view_images: torch.Tensor,
text_tokens: torch.Tensor,
proprio_state: torch.Tensor,
actions: torch.Tensor,
advantages: torch.Tensor) -> torch.Tensor:
"""Calculate reinforcement learning loss"""
# Get action probabilities (in practice, model should output both actions and log_probs)
new_actions = self.model(multi_view_images, text_tokens, proprio_state)
# Simple policy gradient loss
# In practice, you'd want log probabilities for proper policy gradient
action_diff = (actions - new_actions) ** 2
policy_loss = (action_diff.mean(dim=1) * advantages).mean()
return policy_loss
def train_step(self,
imitation_batch: Optional[Dict] = None,
reinforcement_batch: Optional[Dict] = None,
rewards: Optional[List[float]] = None) -> Dict[str, float]:
"""Single training step combining imitation and reinforcement learning"""
total_loss = 0.0
losses = {}
# Imitation learning component
if imitation_batch is not None:
im_loss = self.imitation_loss(
imitation_batch['visual_data'],
imitation_batch['language_data'],
imitation_batch['robot_state'],
imitation_batch['action_data']
)
im_loss_weighted = self.imitation_weight * im_loss
total_loss += im_loss_weighted
losses['imitation'] = im_loss_weighted.item()
# Reinforcement learning component
if reinforcement_batch is not None and rewards is not None:
# Get current values
with torch.no_grad():
shared_repr = self.model.shared_encoder(
reinforcement_batch['visual_data'],
reinforcement_batch['language_data'],
reinforcement_batch['robot_state']
)
values = self.value_network(shared_repr).squeeze(-1).tolist()
# Compute advantages
advantages = self.compute_advantages(rewards, values)
advantages_tensor = torch.tensor(advantages, dtype=torch.float32, device=shared_repr.device)
# Calculate RL loss
rl_loss = self.reinforcement_loss(
reinforcement_batch['visual_data'],
reinforcement_batch['language_data'],
reinforcement_batch['robot_state'],
reinforcement_batch['action_data'], # Previous actions
advantages_tensor
)
rl_loss_weighted = self.reinforcement_weight * rl_loss
total_loss += rl_loss_weighted
losses['reinforcement'] = rl_loss_weighted.item()
# Update parameters
self.optimizer.zero_grad()
if reinforcement_batch is not None:
self.value_optimizer.zero_grad()
total_loss.backward()
self.optimizer.step()
if reinforcement_batch is not None:
self.value_optimizer.step()
losses['total'] = total_loss.item()
return losses
class HumanoidEnvironmentSimulator:
"""Simulator for humanoid robot environment interactions"""
def __init__(self):
self.current_state = {
'joint_positions': [0.0] * 28,
'joint_velocities': [0.0] * 28,
'end_effector_pos': [0.0, 0.0, 1.0],
'object_positions': {'cup': [1.0, 0.5, 0.8]},
'human_positions': {}
}
self.episode_step = 0
def step(self, action: torch.Tensor) -> Tuple[Dict, float, bool, Dict]:
"""Take a step in the environment"""
# Convert action to joint positions
joint_commands = action.detach().cpu().numpy()
# Update state based on action (simplified physics)
self._apply_action(joint_commands)
# Calculate reward
reward = self._calculate_reward()
# Check if episode is done
done = self.episode_step >= 100 # Max 100 steps
self.episode_step += 1
# Get next state
next_state = self._get_state_dict()
return next_state, reward, done, {}
def _apply_action(self, joint_commands: np.ndarray):
"""Apply action to update robot state"""
# Simplified physics update
for i in range(min(len(self.current_state['joint_positions']), len(joint_commands))):
self.current_state['joint_positions'][i] += joint_commands[i] * 0.01 # Small step
# Apply joint limits
self.current_state['joint_positions'][i] = np.clip(
self.current_state['joint_positions'][i], -2.5, 2.5
)
def _calculate_reward(self) -> float:
"""Calculate reward for current state"""
# Example: reward for reaching a target position
target_pos = [1.0, 0.5, 0.8] # Position of the cup
current_pos = self.current_state['end_effector_pos']
distance = np.linalg.norm(np.array(target_pos) - np.array(current_pos))
reward = -distance # Negative distance as reward
# Add bonus for being close to target
if distance < 0.1:
reward += 10.0
return reward
def _get_state_dict(self) -> Dict:
"""Get current state as dictionary"""
return {
'joint_positions': torch.tensor(self.current_state['joint_positions'], dtype=torch.float32),
'joint_velocities': torch.tensor(self.current_state['joint_velocities'], dtype=torch.float32),
'end_effector_pos': torch.tensor(self.current_state['end_effector_pos'], dtype=torch.float32),
'object_positions': self.current_state['object_positions']
}
def reset(self):
"""Reset environment to initial state"""
self.current_state = {
'joint_positions': [0.0] * 28,
'joint_velocities': [0.0] * 28,
'end_effector_pos': [0.0, 0.0, 1.0],
'object_positions': {'cup': [1.0, 0.5, 0.8]},
'human_positions': {}
}
self.episode_step = 0
def example_imitation_reinforcement():
"""Example combining imitation and reinforcement learning"""
# Create model and trainer
model = HumanoidVLAModel(action_dim=28)
trainer = ImitationReinforcementTrainer(model)
# Create environment simulator
env = HumanoidEnvironmentSimulator()
print("Starting imitation + reinforcement learning...")
# Simulate training loop
for episode in range(5): # Few episodes for demo
env.reset()
episode_rewards = []
episode_values = []
# Generate imitation data (simulated expert demonstrations)
imitation_data = {
'visual_data': torch.randn(1, 3, 3, 224, 224),
'language_data': torch.randint(0, 10000, (1, 10)),
'robot_state': torch.randn(1, 28 * 2),
'action_data': torch.randn(1, 28) # Expert action
}
# Run RL episode
done = False
state = env._get_state_dict()
while not done and len(episode_rewards) < 20: # Limit steps for demo
# Get action from model
with torch.no_grad():
# Create dummy visual and language inputs for demo
visual_input = torch.randn(1, 3, 3, 224, 224)
language_input = torch.randint(0, 10000, (1, 10))
proprio_input = torch.cat([
state['joint_positions'],
state['joint_velocities']
]).unsqueeze(0)
action = model(visual_input, language_input, proprio_input)
# Take step in environment
next_state, reward, done, info = env.step(action)
episode_rewards.append(reward)
# Get value estimate for advantage calculation
with torch.no_grad():
shared_repr = model.shared_encoder(visual_input, language_input, proprio_input)
value = trainer.value_network(shared_repr).item()
episode_values.append(value)
state = next_state
# Train with both imitation and reinforcement components
if episode_rewards:
losses = trainer.train_step(
imitation_batch=imitation_data,
reinforcement_batch={
'visual_data': visual_input,
'language_data': language_input,
'robot_state': proprio_input,
'action_data': action # Previous action taken
},
rewards=episode_rewards
)
print(f"Episode {episode + 1} - Losses: {losses}, Total Reward: {sum(episode_rewards):.2f}")
example_imitation_reinforcement()
Fine-Tuning and Domain Adaptation
Transfer Learning for Humanoid Platforms
Different humanoid platforms have different kinematic structures, sensors, and capabilities, requiring platform-specific fine-tuning.
class PlatformAdaptiveVLA(nn.Module):
"""VLA model adaptable to different humanoid platforms"""
def __init__(self,
base_model: nn.Module,
platform_config: Dict[str, any],
adaptation_method: str = 'adapter'):
super().__init__()
self.base_model = base_model
self.platform_config = platform_config
self.adaptation_method = adaptation_method
# Platform-specific adaptation layers
if adaptation_method == 'adapter':
self._create_adapter_layers()
elif adaptation_method == 'lora':
self._create_lora_layers()
else:
self.platform_projection = nn.Linear(
platform_config.get('base_action_dim', 28),
platform_config['action_dim']
)
def _create_adapter_layers(self):
"""Create adapter layers for platform adaptation"""
# Vision adapter
self.vision_adapter = nn.Sequential(
nn.Linear(self.base_model.vision_encoder.hidden_dim,
self.base_model.vision_encoder.hidden_dim // 2),
nn.ReLU(),
nn.Linear(self.base_model.vision_encoder.hidden_dim // 2,
self.base_model.vision_encoder.hidden_dim)
)
# Language adapter
self.language_adapter = nn.Sequential(
nn.Linear(self.base_model.shared_dim, self.base_model.shared_dim // 2),
nn.ReLU(),
nn.Linear(self.base_model.shared_dim // 2, self.base_model.shared_dim)
)
# Action adapter
self.action_adapter = nn.Sequential(
nn.Linear(self.base_model.shared_dim, self.base_model.shared_dim // 2),
nn.ReLU(),
nn.Linear(self.base_model.shared_dim // 2,
self.platform_config['action_dim'])
)
def _create_lora_layers(self):
"""Create LoRA (Low-Rank Adaptation) layers"""
# LoRA implementation for vision encoder
vision_dim = self.base_model.vision_encoder.hidden_dim
lora_rank = 8 # Low rank for efficiency
self.vision_lora_A = nn.Linear(vision_dim, lora_rank, bias=False)
self.vision_lora_B = nn.Linear(lora_rank, vision_dim, bias=False)
# Initialize LoRA weights
nn.init.zeros_(self.vision_lora_B.weight)
# Similar for other components...
def forward(self,
multi_view_images: torch.Tensor,
text_tokens: torch.Tensor,
proprio_state: torch.Tensor) -> torch.Tensor:
"""Forward pass with platform adaptation"""
if self.adaptation_method == 'adapter':
# Get base representation
base_repr = self.base_model(multi_view_images, text_tokens, proprio_state)
# Apply adapters
vision_adapted = self.vision_adapter(base_repr)
language_adapted = self.language_adapter(base_repr)
combined_repr = (vision_adapted + language_adapted) / 2
# Generate platform-specific actions
platform_actions = self.action_adapter(combined_repr)
return platform_actions
elif self.adaptation_method == 'lora':
# Base forward pass
base_output = self.base_model(multi_view_images, text_tokens, proprio_state)
# Apply LoRA adaptation
lora_correction = self.vision_lora_B(self.vision_lora_A(base_output))
adapted_output = base_output + lora_correction
return adapted_output
else:
# Direct projection method
base_actions = self.base_model(multi_view_images, text_tokens, proprio_state)
platform_actions = self.platform_projection(base_actions)
return platform_actions
class DomainAdaptationTrainer:
"""Trainer for domain adaptation of VLA models"""
def __init__(self,
model: PlatformAdaptiveVLA,
source_dataset: Dataset,
target_dataset: Dataset,
learning_rate: float = 1e-5):
self.model = model
self.source_dataset = source_dataset
self.target_dataset = target_dataset
self.optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
# Domain classifier for domain adaptation
self.domain_classifier = nn.Sequential(
nn.Linear(512, 128),
nn.ReLU(),
nn.Linear(128, 2) # Source vs Target
)
self.domain_optimizer = torch.optim.AdamW(
self.domain_classifier.parameters(), lr=learning_rate
)
self.criterion = nn.CrossEntropyLoss()
self.mse_criterion = nn.MSELoss()
def train_step(self,
source_batch: Optional[Dict] = None,
target_batch: Optional[Dict] = None,
train_domain_classifier: bool = True) -> Dict[str, float]:
"""Training step for domain adaptation"""
losses = {}
# Task-specific loss on source domain
if source_batch is not None:
source_pred = self.model(
source_batch['visual_data'],
source_batch['language_data'],
source_batch['robot_state']
)
source_loss = self.mse_criterion(
source_pred, source_batch['action_data']
)
# Backpropagate task loss
self.optimizer.zero_grad()
source_loss.backward()
self.optimizer.step()
losses['source_task'] = source_loss.item()
# Domain adaptation loss
if source_batch is not None and target_batch is not None:
# Get features from source and target
with torch.no_grad():
source_features = self.model.base_model.shared_encoder(
source_batch['visual_data'],
source_batch['language_data'],
source_batch['robot_state']
)
target_features = self.model.base_model.shared_encoder(
target_batch['visual_data'],
target_batch['language_data'],
target_batch['robot_state']
)
# Train domain classifier to distinguish domains
if train_domain_classifier:
source_labels = torch.zeros(source_features.size(0), dtype=torch.long)
target_labels = torch.ones(target_features.size(0), dtype=torch.long)
all_features = torch.cat([source_features, target_features], dim=0)
all_labels = torch.cat([source_labels, target_labels], dim=0)
domain_pred = self.domain_classifier(all_features.detach())
domain_loss = self.criterion(domain_pred, all_labels)
self.domain_optimizer.zero_grad()
domain_loss.backward()
self.domain_optimizer.step()
losses['domain_classifier'] = domain_loss.item()
# Train model to fool domain classifier (domain confusion)
domain_pred_target = self.domain_classifier(target_features)
domain_labels_target = torch.zeros(target_features.size(0), dtype=torch.long) # Try to look like source
confusion_loss = self.criterion(domain_pred_target, domain_labels_target)
self.optimizer.zero_grad()
confusion_loss.backward()
self.optimizer.step()
losses['domain_confusion'] = confusion_loss.item()
return losses
def example_platform_adaptation():
"""Example of platform adaptation"""
# Create base model
base_model = HumanoidVLAModel(action_dim=28)
# Platform configurations
platform_configs = {
'atlas': {
'action_dim': 28,
'joint_names': [f'joint_{i}' for i in range(28)],
'sensor_config': {'imu': True, 'force_torque': True}
},
'valkyrie': {
'action_dim': 36,
'joint_names': [f'joint_{i}' for i in range(36)],
'sensor_config': {'imu': True, 'force_torque': True, 'torso_imu': True}
},
'nao': {
'action_dim': 25,
'joint_names': [f'joint_{i}' for i in range(25)],
'sensor_config': {'imu': True, 'fsr': True}
}
}
# Adapt model to different platforms
for platform_name, config in platform_configs.items():
print(f"Adapting model to {platform_name} platform...")
adapted_model = PlatformAdaptiveVLA(
base_model=base_model,
platform_config=config,
adaptation_method='adapter'
)
print(f" Original action dim: 28 -> {config['action_dim']}")
print(f" Number of trainable params: {sum(p.numel() for p in adapted_model.parameters() if p.requires_grad)}")
# Example domain adaptation training
print("\nStarting domain adaptation...")
# Mock datasets for source and target domains
class MockDataset(Dataset):
def __init__(self, size=50):
self.size = size
def __len__(self):
return self.size
def __getitem__(self, idx):
return {
'visual_data': torch.randn(3, 3, 224, 224),
'language_data': torch.randint(0, 10000, (10,)),
'robot_state': torch.randn(28 * 2),
'action_data': torch.randn(28)
}
source_dataset = MockDataset(30)
target_dataset = MockDataset(30)
# Create adapted model for target platform
target_model = PlatformAdaptiveVLA(
base_model=base_model,
platform_config=platform_configs['valkyrie'],
adaptation_method='adapter'
)
# Create domain adaptation trainer
trainer = DomainAdaptationTrainer(
model=target_model,
source_dataset=source_dataset,
target_dataset=target_dataset
)
# Simulate adaptation training
for epoch in range(3):
# Get batches
source_loader = DataLoader(source_dataset, batch_size=2, shuffle=True)
target_loader = DataLoader(target_dataset, batch_size=2, shuffle=True)
source_batch = next(iter(source_loader))
target_batch = next(iter(target_loader))
# Train
losses = trainer.train_step(source_batch, target_batch)
print(f"Epoch {epoch + 1} adaptation losses: {losses}")
example_platform_adaptation()
Deployment and Optimization
Real-Time Inference Optimization
Deploying VLA models on humanoid robots requires optimization for real-time performance and computational efficiency.
class OptimizedVLAInference:
"""Optimized inference engine for humanoid VLA models"""
def __init__(self, model: nn.Module, optimization_level: str = 'balanced'):
self.model = model
self.optimization_level = optimization_level
self.compiled_model = None
self.inference_cache = {}
# Apply optimizations based on level
self._apply_optimizations()
def _apply_optimizations(self):
"""Apply various optimizations based on level"""
if self.optimization_level == 'performance':
# Maximum performance optimizations
self._optimize_for_performance()
elif self.optimization_level == 'efficiency':
# Power and memory efficiency optimizations
self._optimize_for_efficiency()
else: # balanced
# Balanced optimizations
self._optimize_balanced()
def _optimize_for_performance(self):
"""Apply performance-focused optimizations"""
import torch._dynamo as dynamo
# Compile model with TorchDynamo for performance
self.compiled_model = dynamo.optimize("inductor")(self.model)
# Enable tensor cores and mixed precision
torch.backends.cudnn.benchmark = True
torch.backends.cuda.matmul.allow_tf32 = True
def _optimize_for_efficiency(self):
"""Apply efficiency-focused optimizations"""
# Quantize model for reduced memory and computation
self.model = torch.quantization.quantize_dynamic(
self.model, {nn.Linear, nn.Conv2d}, dtype=torch.qint8
)
def _optimize_balanced(self):
"""Apply balanced optimizations"""
# Use TorchScript for optimized execution
self.model.eval()
# self.compiled_model = torch.jit.trace(self.model, example_inputs) # Would need example inputs
def preprocess_inputs(self,
images: np.ndarray,
text: str,
robot_state: np.ndarray) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""Preprocess inputs for efficient inference"""
# Preprocess images
if isinstance(images, np.ndarray):
images = torch.from_numpy(images).float().permute(0, 3, 1, 2) / 255.0
# Preprocess text
text_tokens = self._tokenize_text(text)
# Preprocess robot state
if isinstance(robot_state, np.ndarray):
robot_state = torch.from_numpy(robot_state).float()
return images, text_tokens, robot_state
def _tokenize_text(self, text: str) -> torch.Tensor:
"""Convert text to token tensor"""
# Simple tokenization (in practice, use proper tokenizer)
vocab = {
'move': 1, 'forward': 2, 'backward': 3, 'left': 4, 'right': 5,
'pick': 6, 'up': 7, 'place': 8, 'down': 9, 'go': 10,
'to': 11, 'the': 12, 'a': 13, 'an': 14, 'object': 15,
'cup': 16, 'box': 17, 'table': 18, 'chair': 19, 'kitchen': 20
}
tokens = []
for word in text.lower().split():
clean_word = ''.join(c for c in word if c.isalnum())
tokens.append(vocab.get(clean_word, 0))
# Pad to fixed length
tokens = tokens[:20] + [0] * max(0, 20 - len(tokens))
return torch.tensor(tokens, dtype=torch.long).unsqueeze(0)
def __call__(self,
multi_view_images: torch.Tensor,
text_tokens: torch.Tensor,
proprio_state: torch.Tensor) -> torch.Tensor:
"""Optimized inference call"""
# Use compiled model if available
if self.compiled_model is not None:
with torch.no_grad():
return self.compiled_model(multi_view_images, text_tokens, proprio_state)
else:
with torch.no_grad():
return self.model(multi_view_images, text_tokens, proprio_state)
class RealTimeVLAExecutor:
"""Real-time execution system for humanoid VLA"""
def __init__(self, optimized_model: OptimizedVLAInference, control_freq: float = 50.0):
self.model = optimized_model
self.control_freq = control_freq
self.control_period = 1.0 / control_freq
# Real-time scheduling
self.task_queue = queue.Queue()
self.is_running = False
self.executor_thread = None
# Performance monitoring
self.inference_times = []
self.control_delays = []
def start_execution(self):
"""Start real-time execution loop"""
self.is_running = True
self.executor_thread = threading.Thread(target=self._execution_loop)
self.executor_thread.start()
def stop_execution(self):
"""Stop real-time execution"""
self.is_running = False
if self.executor_thread:
self.executor_thread.join()
def _execution_loop(self):
"""Main real-time execution loop"""
import time
while self.is_running:
start_time = time.time()
try:
# Get latest command and state
if not self.task_queue.empty():
task_data = self.task_queue.get_nowait()
multi_view_images = task_data['images']
text_command = task_data['command']
proprio_state = task_data['state']
# Preprocess inputs
images, text_tokens, state_tensor = self.model.preprocess_inputs(
multi_view_images, text_command, proprio_state
)
# Run inference
inference_start = time.time()
action = self.model(images.unsqueeze(0), text_tokens, state_tensor.unsqueeze(0))
inference_time = time.time() - inference_start
self.inference_times.append(inference_time)
# Apply action to robot (simulated)
self._apply_action_to_robot(action.squeeze())
# Maintain control frequency
execution_time = time.time() - start_time
sleep_time = max(0, self.control_period - execution_time)
if sleep_time > 0:
time.sleep(sleep_time)
self.control_delays.append(execution_time)
except queue.Empty:
# No tasks available, maintain timing
time.sleep(self.control_period)
except Exception as e:
print(f"Error in execution loop: {e}")
time.sleep(0.01) # Brief pause before continuing
def _apply_action_to_robot(self, action: torch.Tensor):
"""Apply computed action to humanoid robot (simulated)"""
# This would interface with the actual robot
# For simulation, just print the action
action_np = action.detach().cpu().numpy()
print(f"Applied action with norm: {np.linalg.norm(action_np):.3f}")
def get_performance_metrics(self) -> Dict[str, float]:
"""Get real-time performance metrics"""
if not self.inference_times:
return {'avg_inference_time': 0.0, 'avg_control_delay': 0.0}
return {
'avg_inference_time': np.mean(self.inference_times),
'std_inference_time': np.std(self.inference_times),
'avg_control_delay': np.mean(self.control_delays),
'control_frequency_achieved': 1.0 / np.mean(self.control_delays) if self.control_delays else 0.0,
'inference_percent_of_cycle': np.mean(self.inference_times) / self.control_period * 100 if self.control_delays else 0.0
}
def example_real_time_deployment():
"""Example of real-time deployment"""
# Create and optimize model
base_model = HumanoidVLAModel(action_dim=28)
optimized_model = OptimizedVLAInference(base_model, optimization_level='performance')
# Create real-time executor
executor = RealTimeVLAExecutor(optimized_model, control_freq=50.0) # 50 Hz control
print("Starting real-time VLA execution...")
executor.start_execution()
# Simulate sending tasks to the executor
import time
for i in range(10): # Send 10 tasks
task_data = {
'images': np.random.rand(3, 224, 224, 3), # 3 camera views
'command': 'move forward' if i % 2 == 0 else 'pick up object',
'state': np.random.rand(28 * 2) # joint positions + velocities
}
executor.task_queue.put(task_data)
time.sleep(0.1) # Send tasks at 10 Hz
# Let it run a bit more
time.sleep(1.0)
# Stop execution and get metrics
executor.stop_execution()
metrics = executor.get_performance_metrics()
print(f"Performance metrics: {metrics}")
example_real_time_deployment()
Evaluation and Validation
Comprehensive Evaluation Framework
Evaluating VLA models for humanoid robotics requires comprehensive assessment across multiple dimensions.
class HumanoidVLAEvaluator:
"""Comprehensive evaluation framework for humanoid VLA models"""
def __init__(self, model: nn.Module, robot_interface = None):
self.model = model
self.robot_interface = robot_interface
self.metrics = {}
# Evaluation components
self.task_performance = TaskPerformanceEvaluator()
self.safety_evaluator = SafetyEvaluator()
self.interaction_evaluator = InteractionEvaluator()
self.efficiency_evaluator = EfficiencyEvaluator()
def evaluate_complete_system(self, test_scenarios: List[Dict]) -> Dict[str, any]:
"""Evaluate the complete VLA system"""
results = {
'task_performance': {},
'safety_metrics': {},
'interaction_quality': {},
'efficiency_metrics': {},
'overall_score': 0.0
}
for scenario in test_scenarios:
scenario_results = self._evaluate_single_scenario(scenario)
# Aggregate results
for key, value in scenario_results.items():
if key not in results:
results[key] = {}
results[key].update(value)
# Calculate overall score
results['overall_score'] = self._calculate_overall_score(results)
return results
def _evaluate_single_scenario(self, scenario: Dict) -> Dict[str, any]:
"""Evaluate a single test scenario"""
scenario_results = {}
# Task performance evaluation
if 'task_description' in scenario:
task_results = self.task_performance.evaluate(
self.model,
scenario['task_description'],
scenario.get('success_criteria', {})
)
scenario_results['task_performance'] = task_results
# Safety evaluation (if robot interface available)
if self.robot_interface and 'safety_check' in scenario:
safety_results = self.safety_evaluator.evaluate(
self.model,
self.robot_interface,
scenario['safety_check']
)
scenario_results['safety_metrics'] = safety_results
# Interaction quality evaluation
if 'interaction_sequence' in scenario:
interaction_results = self.interaction_evaluator.evaluate(
self.model,
scenario['interaction_sequence']
)
scenario_results['interaction_quality'] = interaction_results
# Efficiency evaluation
efficiency_results = self.efficiency_evaluator.evaluate(
self.model,
scenario.get('computation_limits', {})
)
scenario_results['efficiency_metrics'] = efficiency_results
return scenario_results
def _calculate_overall_score(self, results: Dict) -> float:
"""Calculate overall system score"""
# Weighted combination of different metrics
weights = {
'task_success': 0.4,
'safety': 0.25,
'interaction_quality': 0.2,
'efficiency': 0.15
}
# Calculate weighted score
total_score = 0.0
total_weight = 0.0
if 'task_performance' in results:
task_score = results['task_performance'].get('success_rate', 0.0)
total_score += weights['task_success'] * task_score
total_weight += weights['task_success']
if 'safety_metrics' in results:
safety_score = results['safety_metrics'].get('safety_score', 1.0)
total_score += weights['safety'] * safety_score
total_weight += weights['safety']
if 'interaction_quality' in results:
interaction_score = results['interaction_quality'].get('quality_score', 0.5)
total_score += weights['interaction_quality'] * interaction_score
total_weight += weights['interaction_quality']
if 'efficiency_metrics' in results:
efficiency_score = results['efficiency_metrics'].get('efficiency_score', 0.5)
total_score += weights['efficiency'] * efficiency_score
total_weight += weights['efficiency']
return total_score / total_weight if total_weight > 0 else 0.0
class TaskPerformanceEvaluator:
"""Evaluate task performance of VLA models"""
def __init__(self):
self.task_success_count = 0
self.total_attempts = 0
def evaluate(self, model, task_description: str, success_criteria: Dict) -> Dict[str, float]:
"""Evaluate task performance"""
# Simulate task execution
task_success = self._simulate_task_execution(model, task_description, success_criteria)
if task_success:
self.task_success_count += 1
self.total_attempts += 1
success_rate = self.task_success_count / self.total_attempts if self.total_attempts > 0 else 0.0
return {
'success_rate': success_rate,
'attempts': self.total_attempts,
'successes': self.task_success_count,
'task_complexity': self._assess_task_complexity(task_description)
}
def _simulate_task_execution(self, model, task_description: str, success_criteria: Dict) -> bool:
"""Simulate task execution for evaluation"""
# This would connect to real robot or simulator in practice
# For demo, return random success based on task complexity
complexity = self._assess_task_complexity(task_description)
return np.random.random() > complexity * 0.3 # Higher complexity = lower success rate
def _assess_task_complexity(self, task_description: str) -> float:
"""Assess task complexity (0-1 scale)"""
complexity_keywords = {
'simple': ['go', 'stop', 'wait'],
'medium': ['pick', 'place', 'navigate'],
'complex': ['assemble', 'manipulate', 'interact']
}
task_lower = task_description.lower()
if any(keyword in task_lower for keyword in complexity_keywords['complex']):
return 0.8
elif any(keyword in task_lower for keyword in complexity_keywords['medium']):
return 0.5
else:
return 0.2
class SafetyEvaluator:
"""Evaluate safety aspects of VLA models"""
def __init__(self):
self.safety_violations = 0
self.total_safety_checks = 0
def evaluate(self, model, robot_interface, safety_requirements: Dict) -> Dict[str, float]:
"""Evaluate safety compliance"""
# Check various safety aspects
safety_checks = [
self._check_joint_limits(model, robot_interface),
self._check_collision_avoidance(model, robot_interface),
self._check_stability(model, robot_interface),
self._check_force_limits(model, robot_interface)
]
safe_checks = sum(safety_checks)
total_checks = len(safety_checks)
safety_score = safe_checks / total_checks if total_checks > 0 else 1.0
return {
'safety_score': safety_score,
'passed_checks': safe_checks,
'total_checks': total_checks,
'safety_violations': self.safety_violations
}
def _check_joint_limits(self, model, robot_interface) -> bool:
"""Check if actions respect joint limits"""
return True # Simplified
def _check_collision_avoidance(self, model, robot_interface) -> bool:
"""Check if actions avoid collisions"""
return True # Simplified
def _check_stability(self, model, robot_interface) -> bool:
"""Check if actions maintain robot stability"""
return True # Simplified
def _check_force_limits(self, model, robot_interface) -> bool:
"""Check if actions respect force/torque limits"""
return True # Simplified
class InteractionEvaluator:
"""Evaluate human-robot interaction quality"""
def __init__(self):
self.interaction_metrics = []
def evaluate(self, model, interaction_sequence: List[Dict]) -> Dict[str, float]:
"""Evaluate interaction quality"""
# Evaluate each interaction in the sequence
for interaction in interaction_sequence:
metric = self._evaluate_single_interaction(model, interaction)
self.interaction_metrics.append(metric)
# Calculate average metrics
if self.interaction_metrics:
avg_understanding = np.mean([m['understanding_score'] for m in self.interaction_metrics])
avg_responsiveness = np.mean([m['responsiveness_score'] for m in self.interaction_metrics])
avg_naturalness = np.mean([m['naturalness_score'] for m in self.interaction_metrics])
else:
avg_understanding = avg_responsiveness = avg_naturalness = 0.0
quality_score = (avg_understanding + avg_responsiveness + avg_naturalness) / 3
return {
'quality_score': quality_score,
'understanding_score': avg_understanding,
'responsiveness_score': avg_responsiveness,
'naturalness_score': avg_naturalness,
'total_interactions': len(interaction_sequence)
}
def _evaluate_single_interaction(self, model, interaction: Dict) -> Dict[str, float]:
"""Evaluate a single interaction"""
# Simulated evaluation
return {
'understanding_score': np.random.uniform(0.7, 1.0),
'responsiveness_score': np.random.uniform(0.6, 1.0),
'naturalness_score': np.random.uniform(0.5, 1.0)
}
class EfficiencyEvaluator:
"""Evaluate computational efficiency"""
def __init__(self):
self.inference_times = []
self.memory_usage = []
def evaluate(self, model, computation_limits: Dict) -> Dict[str, float]:
"""Evaluate computational efficiency"""
import time
# Measure inference time
test_inputs = self._create_test_inputs()
for _ in range(10): # Test multiple times for average
start_time = time.time()
with torch.no_grad():
_ = model(*test_inputs)
inference_time = time.time() - start_time
self.inference_times.append(inference_time)
avg_inference_time = np.mean(self.inference_times) if self.inference_times else 0.0
efficiency_score = min(1.0, 0.1 / avg_inference_time) if avg_inference_time > 0 else 0.0 # Higher is better
return {
'efficiency_score': efficiency_score,
'avg_inference_time': avg_inference_time,
'max_inference_time': max(self.inference_times) if self.inference_times else 0.0,
'min_inference_time': min(self.inference_times) if self.inference_times else 0.0,
'std_inference_time': np.std(self.inference_times) if len(self.inference_times) > 1 else 0.0
}
def _create_test_inputs(self) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""Create test inputs for efficiency evaluation"""
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)
return multi_view_images, text_tokens, proprio_state
def example_comprehensive_evaluation():
"""Example of comprehensive VLA evaluation"""
# Create model
model = HumanoidVLAModel(action_dim=28)
# Create evaluator
evaluator = HumanoidVLAEvaluator(model)
# Define test scenarios
test_scenarios = [
{
'task_description': 'navigate to kitchen and pick up cup',
'success_criteria': {'reach_target': True, 'grasp_object': True},
'interaction_sequence': [
{'command': 'go to kitchen', 'expected_action': 'navigation'},
{'command': 'pick up cup', 'expected_action': 'manipulation'}
],
'computation_limits': {'max_inference_time': 0.05} # 50ms
},
{
'task_description': 'greet person and shake hands',
'success_criteria': {'social_interaction': True, 'safety_compliance': True},
'interaction_sequence': [
{'command': 'wave hello', 'expected_action': 'social'}
],
'computation_limits': {'max_inference_time': 0.05}
}
]
# Run evaluation
results = evaluator.evaluate_complete_system(test_scenarios)
print("Comprehensive VLA Evaluation Results:")
print("=" * 50)
for key, value in results.items():
print(f"{key}: {value}")
print(f"\nOverall System Score: {results['overall_score']:.3f}")
example_comprehensive_evaluation()
Summary
Training Vision-Language-Action (VLA) models for humanoid robotics is a complex but rewarding endeavor that requires careful consideration of data collection, model architecture, training strategies, and deployment considerations. This chapter has covered:
Key Takeaways:
- Data Collection: Multi-view visual data, proprioceptive information, and human demonstrations are crucial for humanoid VLA training
- Model Architecture: Multi-view vision encoders, hierarchical action spaces, and multimodal fusion are essential components
- Training Strategies: Multi-task learning, imitation learning combined with reinforcement learning, and curriculum learning improve performance
- Platform Adaptation: Domain adaptation and platform-specific fine-tuning enable deployment across different humanoid platforms
- Real-time Deployment: Optimization techniques and real-time execution frameworks are necessary for practical deployment
- Comprehensive Evaluation: Multi-dimensional evaluation covering task performance, safety, interaction quality, and efficiency is essential
Best Practices:
- Collect diverse, high-quality training data that reflects real-world usage scenarios
- Use hierarchical architectures that can operate at multiple levels of abstraction
- Combine imitation learning with reinforcement learning for robust policy learning
- Apply domain adaptation techniques for cross-platform deployment
- Optimize for real-time performance while maintaining safety
- Implement comprehensive evaluation frameworks that assess all important dimensions
The successful training and deployment of VLA models for humanoid robots enables natural, intuitive human-robot interaction and opens up new possibilities for collaborative robotics in human environments.
Next Steps
In the next chapter, we'll explore advanced topics in VLA for humanoid robotics, including social interaction capabilities, multi-human scenarios, and integration with cognitive architectures for truly intelligent humanoid behavior.
Estimated Reading Time: 30 minutes