docs/modules/isaac/chapter4.md

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Chapter 4: Advanced AI-Robot Brain Techniques

Learning Objectives

• Understand advanced AI techniques for humanoid robot perception and decision making
• Learn about reinforcement learning for humanoid locomotion
• Explore deep learning integration with Isaac ROS perception
• Implement adaptive control systems using AI
• Understand neural architecture search for robotics applications

Introduction to Advanced AI Techniques

Humanoid robots require sophisticated AI systems to perceive, reason, and act in complex environments. This chapter explores advanced techniques that go beyond basic perception and navigation, focusing on systems that can learn, adapt, and make complex decisions.

Key Areas of Advanced AI for Humanoid Robotics

1. Learning-based Locomotion: Using AI to develop adaptive walking patterns
2. Perception-Action Integration: Deep learning systems that connect perception to action
3. Adaptive Control: AI systems that adjust control parameters in real-time
4. Hierarchical Decision Making: Multi-level AI systems for complex tasks
5. Sim-to-Real Transfer: Techniques to transfer learned behaviors from simulation to real robots

Reinforcement Learning for Humanoid Locomotion

Reinforcement Learning (RL) has shown remarkable success in developing robust humanoid locomotion controllers. Unlike traditional control methods, RL can learn complex gait patterns and adapt to various terrains.

Deep Reinforcement Learning Framework

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
from collections import deque
import random

class ActorNetwork(nn.Module):
    """Actor network for humanoid control policy"""
    def __init__(self, state_dim, action_dim, hidden_dim=256):
        super(ActorNetwork, self).__init__()
        
        self.network = nn.Sequential(
            nn.Linear(state_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, action_dim),
            nn.Tanh()  # Actions are clamped to [-1, 1]
        )
    
    def forward(self, state):
        return self.network(state)

class CriticNetwork(nn.Module):
    """Critic network for value estimation"""
    def __init__(self, state_dim, action_dim, hidden_dim=256):
        super(CriticNetwork, self).__init__()
        
        self.network = nn.Sequential(
            nn.Linear(state_dim + action_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, 1)
        )
    
    def forward(self, state, action):
        x = torch.cat([state, action], dim=1)
        return self.network(x)

class HumanoidRLAgent:
    def __init__(self, state_dim, action_dim, lr=3e-4, gamma=0.99, tau=0.005):
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        
        # Networks
        self.actor = ActorNetwork(state_dim, action_dim).to(self.device)
        self.critic = CriticNetwork(state_dim, action_dim).to(self.device)
        self.target_actor = ActorNetwork(state_dim, action_dim).to(self.device)
        self.target_critic = CriticNetwork(state_dim, action_dim).to(self.device)
        
        # Optimizers
        self.actor_optimizer = optim.Adam(self.actor.parameters(), lr=lr)
        self.critic_optimizer = optim.Adam(self.critic.parameters(), lr=lr)
        
        # Hyperparameters
        self.gamma = gamma  # Discount factor
        self.tau = tau      # Soft update parameter
        self.action_dim = action_dim
        
        # Initialize target networks
        self.hard_update(self.target_actor, self.actor)
        self.hard_update(self.target_critic, self.critic)
    
    def hard_update(self, target, source):
        """Hard update target network with source parameters"""
        for target_param, param in zip(target.parameters(), source.parameters()):
            target_param.data.copy_(param.data)
    
    def soft_update(self, target, source):
        """Soft update target network with source parameters"""
        for target_param, param in zip(target.parameters(), source.parameters()):
            target_param.data.copy_(self.tau * param.data + (1.0 - self.tau) * target_param.data)
    
    def select_action(self, state, add_noise=False, noise_scale=0.1):
        """Select action with exploration noise"""
        state_tensor = torch.FloatTensor(state).unsqueeze(0).to(self.device)
        
        with torch.no_grad():
            action = self.actor(state_tensor)
        
        if add_noise:
            noise = torch.randn_like(action) * noise_scale
            action = torch.clamp(action + noise, -1, 1)
        
        return action.cpu().numpy()[0]
    
    def update(self, replay_buffer, batch_size=100):
        """Update networks with experiences from replay buffer"""
        if len(replay_buffer) < batch_size:
            return
        
        # Sample batch
        states, actions, rewards, next_states, dones = replay_buffer.sample(batch_size)
        
        states = torch.FloatTensor(states).to(self.device)
        actions = torch.FloatTensor(actions).to(self.device)
        rewards = torch.FloatTensor(rewards).unsqueeze(1).to(self.device)
        next_states = torch.FloatTensor(next_states).to(self.device)
        dones = torch.BoolTensor(dones).unsqueeze(1).to(self.device)
        
        # Update critic
        with torch.no_grad():
            next_actions = self.target_actor(next_states)
            next_q_values = self.target_critic(next_states, next_actions)
            target_q_values = rewards + (self.gamma * next_q_values * ~dones)
        
        current_q_values = self.critic(states, actions)
        critic_loss = nn.MSELoss()(current_q_values, target_q_values)
        
        self.critic_optimizer.zero_grad()
        critic_loss.backward()
        self.critic_optimizer.step()
        
        # Update actor
        predicted_actions = self.actor(states)
        actor_loss = -self.critic(states, predicted_actions).mean()
        
        self.actor_optimizer.zero_grad()
        actor_loss.backward()
        self.actor_optimizer.step()
        
        # Soft update target networks
        self.soft_update(self.target_actor, self.actor)
        self.soft_update(self.target_critic, self.critic)

class ReplayBuffer:
    """Experience replay buffer for RL training"""
    def __init__(self, capacity=1000000):
        self.buffer = deque(maxlen=capacity)
    
    def push(self, state, action, reward, next_state, done):
        """Add experience to buffer"""
        self.buffer.append((state, action, reward, next_state, done))
    
    def sample(self, batch_size):
        """Sample batch from buffer"""
        batch = random.sample(self.buffer, batch_size)
        state, action, reward, next_state, done = map(np.stack, zip(*batch))
        return state, action, reward, next_state, done
    
    def __len__(self):
        return len(self.buffer)

Humanoid Environment for RL Training

import gym
from gym import spaces
import numpy as np

class HumanoidLocomotionEnv(gym.Env):
    """Custom environment for humanoid locomotion training"""
    def __init__(self):
        super(HumanoidLocomotionEnv, self).__init__()
        
        # Define action and observation spaces
        # This is a simplified example - real environments would have more complex spaces
        self.action_space = spaces.Box(
            low=-1.0, high=1.0, shape=(19,), dtype=np.float32  # 19 joints
        )
        
        # Observation space: joint positions, velocities, IMU readings
        obs_dim = 48  # Example dimension
        self.observation_space = spaces.Box(
            low=-np.inf, high=np.inf, shape=(obs_dim,), dtype=np.float32
        )
        
        # Humanoid robot interface (simplified)
        self.robot = None  # Would interface with Gazebo, PyBullet, etc.
        
        # Episode parameters
        self.max_episode_steps = 1000
        self.current_step = 0
        self.target_velocity = 0.5  # m/s
        
    def reset(self):
        """Reset the environment"""
        # Reset robot to initial pose
        self._reset_robot()
        self.current_step = 0
        
        # Return initial observation
        return self._get_observation()
    
    def step(self, action):
        """Execute one step with given action"""
        # Apply action to robot
        self._apply_action(action)
        
        # Step simulation
        self._step_simulation()
        
        # Get new observation
        observation = self._get_observation()
        
        # Calculate reward
        reward = self._calculate_reward()
        
        # Check termination
        done = self._is_done()
        info = {}
        
        self.current_step += 1
        
        return observation, reward, done, info
    
    def _get_observation(self):
        """Get current observation from robot"""
        # This would interface with robot's sensors
        # Example observations: joint angles, velocities, IMU data, etc.
        observation = np.zeros(48, dtype=np.float32)  # Simplified
        return observation
    
    def _calculate_reward(self):
        """Calculate reward based on current state"""
        # Reward for forward velocity
        forward_vel_reward = self._get_forward_velocity() * 0.1
        
        # Penalty for energy consumption
        energy_penalty = self._get_energy_consumption() * 0.01
        
        # Reward for maintaining balance
        balance_reward = self._get_balance_score() * 0.5
        
        # Penalty for joint limits violations
        joint_limit_penalty = self._get_joint_limit_violations() * 1.0
        
        total_reward = forward_vel_reward - energy_penalty + balance_reward - joint_limit_penalty
        return max(total_reward, -10.0)  # Clamp reward
    
    def _get_forward_velocity(self):
        """Get forward velocity of the robot"""
        # Would interface with robot's odometry
        return 0.0  # Simplified
    
    def _get_energy_consumption(self):
        """Get energy consumption"""
        # Would calculate based on joint torques and velocities
        return 0.0  # Simplified
    
    def _get_balance_score(self):
        """Get balance score (higher is better)"""
        # Calculate based on COM position, IMU readings, etc.
        return 0.0  # Simplified
    
    def _get_joint_limit_violations(self):
        """Count joint limit violations"""
        # Check current joint positions against limits
        return 0.0  # Simplified
    
    def _is_done(self):
        """Check if episode is done"""
        # Done if fallen, exceeded max steps, or other failure conditions
        return self.current_step >= self.max_episode_steps
    
    def _apply_action(self, action):
        """Apply action to robot"""
        # Convert normalized action to joint commands
        # This would interface with robot controller
        pass
    
    def _step_simulation(self):
        """Step the physics simulation"""
        # This would interface with physics engine
        pass
    
    def _reset_robot(self):
        """Reset robot to initial configuration"""
        # Reset robot pose, velocities, etc.
        pass

Training Loop for Humanoid Locomotion

def train_humanoid_locomotion():
    """Training loop for humanoid locomotion policy"""
    env = HumanoidLocomotionEnv()
    
    # Initialize agent
    state_dim = env.observation_space.shape[0]
    action_dim = env.action_space.shape[0]
    agent = HumanoidRLAgent(state_dim, action_dim)
    
    # Initialize replay buffer
    replay_buffer = ReplayBuffer(capacity=1000000)
    
    # Training parameters
    num_episodes = 2000
    max_steps_per_episode = 1000
    batch_size = 256
    update_every = 50
    
    scores = []
    avg_scores = []
    
    for episode in range(num_episodes):
        state = env.reset()
        episode_reward = 0
        episode_steps = 0
        
        for step in range(max_steps_per_episode):
            # Select action with exploration
            action = agent.select_action(state, add_noise=True, noise_scale=0.1)
            
            # Take action
            next_state, reward, done, info = env.step(action)
            
            # Store experience
            replay_buffer.push(state, action, reward, next_state, done)
            
            # Update agent
            if len(replay_buffer) > batch_size and step % update_every == 0:
                agent.update(replay_buffer, batch_size)
            
            state = next_state
            episode_reward += reward
            episode_steps += 1
            
            if done:
                break
        
        scores.append(episode_reward)
        
        # Calculate average score over last 100 episodes
        if len(scores) >= 100:
            avg_score = sum(scores[-100:]) / 100
            avg_scores.append(avg_score)
        else:
            avg_scores.append(sum(scores) / len(scores))
        
        print(f"Episode {episode}, Score: {episode_reward:.2f}, "
              f"Avg Score: {avg_scores[-1]:.2f}")
    
    # Save trained model
    torch.save(agent.actor.state_dict(), "humanoid_locomotion_actor.pth")
    torch.save(agent.critic.state_dict(), "humanoid_locomotion_critic.pth")
    
    return agent, scores, avg_scores

Isaac ROS Deep Learning Integration

Isaac ROS provides GPU-accelerated deep learning capabilities that can be integrated with humanoid control systems:

Perception-Action Integration

import rclpy
from rclpy.node import Node
from sensor_msgs.msg import Image, CompressedImage, Imu
from geometry_msgs.msg import Twist
import torch
import torchvision.transforms as T
from PIL import Image as PILImage
import io
import cv2

class PerceptionActionNode(Node):
    def __init__(self):
        super().__init__('perception_action_node')
        
        # Subscriptions
        self.image_sub = self.create_subscription(
            Image, '/camera/color/image_raw', self.image_callback, 10)
        self.depth_sub = self.create_subscription(
            Image, '/camera/depth/image_rect_raw', self.depth_callback, 10)
        self.imu_sub = self.create_subscription(
            Imu, '/imu/data', self.imu_callback, 10)
        
        # Publisher for action commands
        self.cmd_vel_pub = self.create_publisher(Twist, '/cmd_vel', 10)
        
        # Load pretrained models
        self.perception_model = self.load_perception_model()
        self.action_model = self.load_action_model()
        
        # Transformation for images
        self.transform = T.Compose([
            T.Resize((224, 224)),
            T.ToTensor(),
            T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
        ])
        
        # State buffers
        self.current_img = None
        self.current_depth = None
        self.current_imu = None
        
        self.get_logger().info('Perception-Action Node initialized')
    
    def load_perception_model(self):
        """Load pretrained perception model"""
        # In practice, this would load a model from Isaac ROS or other source
        # For example, an object detection or segmentation model
        import torchvision.models as models
        model = models.resnet18(pretrained=True)
        model.eval()
        return model
    
    def load_action_model(self):
        """Load action selection model"""
        # This would be a model that maps perceptions to actions
        # Could be the RL policy trained above
        import torch.nn as nn
        
        class ActionModel(nn.Module):
            def __init__(self, perception_features_dim, action_dim):
                super(ActionModel, self).__init__()
                self.fc1 = nn.Linear(perception_features_dim, 256)
                self.relu = nn.ReLU()
                self.fc2 = nn.Linear(256, 128)
                self.action_head = nn.Linear(128, action_dim)
            
            def forward(self, perception_features):
                x = self.relu(self.fc1(perception_features))
                x = self.relu(self.fc2(x))
                action = torch.tanh(self.action_head(x))
                return action
        
        model = ActionModel(512, 2)  # 512 features, 2D action (vx, wz)
        model.eval()
        return model
    
    def image_callback(self, msg):
        """Process incoming image"""
        try:
            # Convert ROS Image to PIL Image
            img_data = np.frombuffer(msg.data, dtype=np.uint8).reshape(
                msg.height, msg.width, -1)
            pil_img = PILImage.fromarray(img_data)
            
            # Process image with perception model
            with torch.no_grad():
                transformed_img = self.transform(pil_img).unsqueeze(0)
                features = self.extract_features(transformed_img)
                
                # Determine action based on perception
                action = self.action_model(features)
                
                # Execute action
                self.publish_action(action)
                    
        except Exception as e:
            self.get_logger().error(f'Error processing image: {str(e)}')
    
    def extract_features(self, img_tensor):
        """Extract features using perception model"""
        # Get intermediate layer features (example)
        # This would depend on the specific model architecture
        with torch.no_grad():
            # Run through convolutional layers to extract features
            x = self.perception_model.conv1(img_tensor)
            x = self.perception_model.bn1(x)
            x = self.perception_model.relu(x)
            x = self.perception_model.maxpool(x)
            
            x = self.perception_model.layer1(x)
            x = self.perception_model.layer2(x)
            x = self.perception_model.layer3(x)
            x = self.perception_model.layer4(x)
            
            # Global average pooling
            x = torch.nn.functional.adaptive_avg_pool2d(x, (1, 1))
            features = torch.flatten(x, 1)
            
            return features
    
    def depth_callback(self, msg):
        """Process depth information"""
        # Process depth data for navigation
        pass
    
    def imu_callback(self, msg):
        """Process IMU data for balance"""
        # Process IMU for balance awareness
        pass
    
    def publish_action(self, action_tensor):
        """Execute action by publishing to robot"""
        cmd_msg = Twist()
        cmd_msg.linear.x = float(action_tensor[0, 0]) * 0.5  # Scale to reasonable velocity
        cmd_msg.angular.z = float(action_tensor[0, 1]) * 0.5  # Scale to reasonable angular velocity
        
        self.cmd_vel_pub.publish(cmd_msg)

Adaptive Control Systems

Adaptive control systems can modify their behavior based on changing conditions or performance:

Model Reference Adaptive Control (MRAC)

class MRACController:
    """Model Reference Adaptive Controller for humanoid robots"""
    def __init__(self, reference_model_params, plant_params):
        self.reference_model = self.initialize_reference_model(reference_model_params)
        self.plant_params = plant_params
        
        # Adaptive parameters
        self.theta = np.zeros(plant_params.size)  # Controller parameters
        self.P = np.eye(plant_params.size) * 100  # Covariance matrix
        self.gamma = 1.0  # Adaptation gain
        
        # State tracking
        self.error = 0.0
        self.prev_error = 0.0
        self.integral_error = 0.0
    
    def initialize_reference_model(self, params):
        """Initialize reference model for desired behavior"""
        # This would create a reference dynamic system
        # For humanoid, this might represent ideal walking dynamics
        class ReferenceModel:
            def __init__(self, params):
                self.params = params
                self.state = 0.0  # Simplified state
            
            def update(self, input_signal):
                # Update reference model state
                # This implements the desired dynamics
                self.state = self.state * 0.9 + input_signal * 0.1  # Simplified
                return self.state
        
        return ReferenceModel(params)
    
    def update(self, measured_output, reference_input):
        """Update controller with new measurements"""
        # Get reference output
        reference_output = self.reference_model.update(reference_input)
        
        # Calculate tracking error
        self.error = reference_output - measured_output
        
        # Compute parameter adjustment
        phi = self.get_regression_vector(measured_output, reference_input)
        adjustment = self.gamma * np.outer(self.P, phi) * self.error
        self.theta += adjustment.flatten()
        
        # Update covariance matrix
        denom = 1 + np.dot(phi, np.dot(self.P, phi))
        self.P = self.P - (np.outer(np.dot(self.P, phi), np.dot(phi, self.P))) / denom
        
        # Compute control signal
        control_signal = np.dot(self.theta, phi)
        
        # Update for next iteration
        self.prev_error = self.error
        
        return control_signal
    
    def get_regression_vector(self, y, r):
        """Get regression vector for adaptation law"""
        # This would depend on the specific plant model
        # For humanoid, this might relate to joint kinematics/dynamics
        phi = np.array([y, r, y*r, y**2, r**2])  # Example features
        return phi[:self.theta.size]  # Trim to match parameter size

Neural Adaptive Control

import torch.nn as nn

class NeuralAdaptiveController(nn.Module):
    """Neural adaptive controller for complex robotic systems"""
    def __init__(self, state_dim, action_dim, hidden_dim=128):
        super(NeuralAdaptiveController, self).__init__()
        
        # Controller network
        self.controller_network = nn.Sequential(
            nn.Linear(state_dim + action_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, action_dim),
        )
        
        # Adaptation network (learns to adjust controller parameters)
        self.adaptation_network = nn.Sequential(
            nn.Linear(state_dim + action_dim + hidden_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim),
        )
        
        # Error prediction network
        self.error_predictor = nn.Sequential(
            nn.Linear(state_dim + action_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, 1),
        )
        
        self.state_dim = state_dim
        self.action_dim = action_dim
        
    def forward(self, state, action, internal_state=None):
        """Forward pass with potential adaptation"""
        if internal_state is None:
            internal_state = torch.zeros(state.size(0), self.hidden_dim)
        
        # Controller output
        controller_output = self.controller_network(torch.cat([state, action], dim=1))
        
        # Adaptation
        adaptation_input = torch.cat([state, action, internal_state], dim=1)
        adaptation = self.adaptation_network(adaptation_input)
        
        # Combined output
        adapted_output = controller_output + 0.1 * adaptation  # Small adaptation influence
        
        # Predict error for learning
        error_prediction = self.error_predictor(torch.cat([state, action], dim=1))
        
        return adapted_output, adaptation, error_prediction

class AdaptiveControlSystem:
    """Complete adaptive control system for humanoid robots"""
    def __init__(self, state_dim, action_dim):
        self.neural_controller = NeuralAdaptiveController(state_dim, action_dim)
        self.optimizer = optim.Adam(self.neural_controller.parameters(), lr=1e-4)
        
        # Performance metrics
        self.performance_history = deque(maxlen=100)
        self.adaptation_activity = 0.0
        
    def compute_control(self, state, desired_action):
        """Compute control action with adaptation"""
        state_tensor = torch.FloatTensor(state).unsqueeze(0)
        action_tensor = torch.FloatTensor(desired_action).unsqueeze(0)
        
        with torch.no_grad():
            control_output, adaptation, error_pred = self.neural_controller(
                state_tensor, action_tensor)
        
        return control_output.numpy()[0]
    
    def update_adaptation(self, state, action, desired_output, actual_output):
        """Update neural adaptation based on performance"""
        state_tensor = torch.FloatTensor(state).unsqueeze(0)
        action_tensor = torch.FloatTensor(action).unsqueeze(0)
        desired_tensor = torch.FloatTensor(desired_output).unsqueeze(0)
        actual_tensor = torch.FloatTensor(actual_output).unsqueeze(0)
        
        # Compute loss
        tracking_error = desired_tensor - actual_tensor
        error_loss = torch.mean(tracking_error ** 2)
        
        # Also train on error prediction
        error_pred = self.neural_controller.error_predictor(
            torch.cat([state_tensor, actual_tensor], dim=1))
        prediction_loss = torch.nn.functional.mse_loss(error_pred, tracking_error)
        
        total_loss = error_loss + 0.1 * prediction_loss
        
        # Update
        self.optimizer.zero_grad()
        total_loss.backward()
        self.optimizer.step()
        
        # Track performance
        self.performance_history.append(error_loss.item())
        
        return error_loss.item()

Hierarchical Decision Making

Complex humanoid tasks often require hierarchical decision-making systems:

Task and Motion Planning (TAMP)

class TaskAndMotionPlanner:
    """Hierarchical planner for complex humanoid tasks"""
    def __init__(self):
        self.task_planner = SymbolicTaskPlanner()
        self.motion_planner = BiPedalMotionPlanner()
        self.high_level_reasoner = HighLevelReasoner()
        
    def plan_task(self, goal_description):
        """Plan high-level task decomposition"""
        # Parse goal description
        goal = self.parse_goal(goal_description)
        
        # Decompose into subtasks
        task_plan = self.task_planner.decompose_task(goal)
        
        # For each task, generate motion plans
        complete_plan = []
        for task in task_plan:
            if task.type == "navigate":
                motion_plan = self.motion_planner.plan_navigate(task.destination)
            elif task.type == "manipulate":
                motion_plan = self.motion_planner.plan_manipulate(
                    task.object, task.destination)
            elif task.type == "communicate":
                motion_plan = self.motion_planner.plan_communicate(task.message)
            
            complete_plan.append({
                'task': task,
                'motion_plan': motion_plan
            })
        
        return complete_plan
    
    def parse_goal(self, goal_description):
        """Parse natural language goal into structured format"""
        # This would use NLP to parse goals
        # Example: "Go to kitchen and bring me a water bottle"
        # Would be parsed into navigate → find_object → grasp → navigate → place
        pass

class SymbolicTaskPlanner:
    """Symbolic task planner using STRIPS-like formalism"""
    def __init__(self):
        # Define operators (actions) and predicates (states)
        self.operators = self.define_operators()
        self.predicates = self.define_predicates()
    
    def define_operators(self):
        """Define available operators for task planning"""
        return {
            'navigate': {
                'preconditions': ['at(X)', 'accessible(Y)'],
                'effects': ['at(Y)', '!at(X)'],
                'cost': 1.0
            },
            'grasp': {
                'preconditions': ['at(X)', 'reachable(X)', 'free_hand()'],
                'effects': ['holding(X)', '!free_hand()'],
                'cost': 0.5
            },
            'place': {
                'preconditions': ['holding(X)', 'at(Y)'],
                'effects': ['!holding(X)', 'placed(X, Y)', 'free_hand()'],
                'cost': 0.5
            }
        }
    
    def decompose_task(self, goal):
        """Decompose goal into sequence of operators"""
        # Implement forward/backward chaining search
        # or use classical planning algorithms
        task_plan = []
        # Simplified implementation
        return task_plan

class BiPedalMotionPlanner:
    """Motion planner specialized for bipedal robots"""
    def __init__(self):
        self.footstep_planner = FootstepPlanner()
        self.balance_controller = BalanceController()
        self.manipulation_planner = ManipulationPlanner()
    
    def plan_navigate(self, destination):
        """Plan navigation motion for bipedal robot"""
        return self.footstep_planner.plan_path(destination)
    
    def plan_manipulate(self, obj, destination):
        """Plan manipulation motion for object"""
        return self.manipulation_planner.plan_grasp_transport_place(obj, destination)
    
    def plan_communicate(self, message):
        """Plan motion for communication (e.g., gestures)"""
        return [{'type': 'gesture', 'motion': 'wave', 'duration': 2.0}]

Sim-to-Real Transfer Techniques

Transferring learned behaviors from simulation to real robots requires special consideration:

Domain Randomization

class DomainRandomization:
    """Domain randomization for sim-to-real transfer"""
    def __init__(self):
        self.randomization_params = {
            'visual': {
                'lighting': (0.5, 2.0),  # Intensity range
                'textures': ['concrete', 'wood', 'carpet', 'grass'],
                'colors': [(0.2, 0.2, 0.2), (0.8, 0.8, 0.8)],  # Dark to light
                'materials': ['matte', 'glossy', 'rough']
            },
            'dynamics': {
                'friction': (0.3, 1.0),
                'mass_variation': (0.8, 1.2),
                'inertia_scaling': (0.9, 1.1),
                'actuator_noise': (0.0, 0.05)
            },
            'sensor': {
                'camera_noise': (0.0, 0.02),
                'imu_drift': (0.0, 0.01),
                'delay_range': (0.01, 0.05)
            }
        }
    
    def randomize_domain(self, sim_env):
        """Randomize simulation domain parameters"""
        # Visual randomization
        lighting_mult = np.random.uniform(
            self.randomization_params['visual']['lighting'][0],
            self.randomization_params['visual']['lighting'][1]
        )
        sim_env.set_lighting_multiplier(lighting_mult)
        
        # Dynamics randomization
        friction = np.random.uniform(
            self.randomization_params['dynamics']['friction'][0],
            self.randomization_params['dynamics']['friction'][1]
        )
        sim_env.set_friction(friction)
        
        # Add sensor noise
        camera_noise = np.random.uniform(
            self.randomization_params['sensor']['camera_noise'][0],
            self.randomization_params['sensor']['camera_noise'][1]
        )
        sim_env.add_camera_noise(camera_noise)
        
        return sim_env

Curriculum Learning

class CurriculumLearning:
    """Curriculum learning for gradual skill acquisition"""
    def __init__(self):
        self.curriculum_levels = [
            {
                'name': 'stationary_balance',
                'difficulty': 0.1,
                'tasks': ['maintain_balance'],
                'rewards': {'balance_time': 1.0, 'fall_penalty': -10.0}
            },
            {
                'name': 'simple_stepping',
                'difficulty': 0.3,
                'tasks': ['step_forward', 'step_backward'],
                'rewards': {'balance_time': 1.0, 'reach_target': 5.0, 'fall_penalty': -10.0}
            },
            {
                'name': 'straight_line_walking',
                'difficulty': 0.5,
                'tasks': ['walk_forward', 'walk_backward'],
                'rewards': {'forward_vel': 1.0, 'energy_efficiency': 0.5, 'balance_time': 0.5, 'fall_penalty': -10.0}
            },
            {
                'name': 'turning',
                'difficulty': 0.7,
                'tasks': ['turn_left', 'turn_right'],
                'rewards': {'heading_accuracy': 2.0, 'balance_time': 0.5, 'energy_efficiency': 0.3, 'fall_penalty': -10.0}
            },
            {
                'name': 'complex_maneuvers',
                'difficulty': 1.0,
                'tasks': ['sidestep', 'walk_over_small_obstacles'],
                'rewards': {'task_completion': 10.0, 'smoothness': 1.0, 'balance_time': 0.5, 'fall_penalty': -10.0}
            }
        ]
        
        self.current_level = 0
        self.level_progress_threshold = 0.8  # 80% success rate to advance
    
    def get_current_tasks(self):
        """Get tasks for current curriculum level"""
        return self.curriculum_levels[self.current_level]['tasks']
    
    def evaluate_performance(self, episode_results):
        """Evaluate agent performance on current level"""
        # Calculate performance metrics based on episode results
        success_rate = self.calculate_success_rate(episode_results)
        
        if success_rate >= self.level_progress_threshold and self.current_level < len(self.curriculum_levels) - 1:
            self.current_level += 1
            print(f"Advancing to curriculum level: {self.curriculum_levels[self.current_level]['name']}")
        
        return success_rate
    
    def calculate_success_rate(self, results):
        """Calculate success rate from episode results"""
        if not results:
            return 0.0
        
        successful_episodes = sum(1 for r in results if r.success)
        return successful_episodes / len(results)

Neural Architecture Search for Robotics

Neural Architecture Search (NAS) can optimize neural networks specifically for robotic tasks:

class RobotNASCandidate:
    """Represents a candidate neural architecture for robotics"""
    def __init__(self, layers_config):
        self.layers_config = layers_config  # List of layer specifications
        self.fitness_score = 0.0
        self.computation_cost = 0.0  # FLOPs or inference time
        
    def build_network(self):
        """Build the neural network from configuration"""
        layers = []
        for layer_config in self.layers_config:
            layer_type = layer_config['type']
            if layer_type == 'conv':
                layers.append(nn.Conv2d(layer_config['in_channels'], 
                                      layer_config['out_channels'],
                                      layer_config['kernel_size']))
            elif layer_type == 'linear':
                layers.append(nn.Linear(layer_config['in_size'], 
                                      layer_config['out_size']))
            elif layer_type == 'residual':
                layers.append(ResidualBlock(layer_config['channels']))
            # Add activation functions
            layers.append(nn.ReLU())
        
        return nn.Sequential(*layers)

class RobotNeuralArchitectureSearch:
    """Neural Architecture Search specialized for robotics applications"""
    def __init__(self, search_space, population_size=50, generations=20):
        self.search_space = search_space
        self.population_size = population_size
        self.generations = generations
        self.population = []
        
        # Robot-specific objectives
        self.objectives = {
            'accuracy': 0.5,
            'latency': 0.3,
            'power_efficiency': 0.2
        }
    
    def initialize_population(self):
        """Initialize random population of architectures"""
        for _ in range(self.population_size):
            config = self.generate_random_architecture()
            candidate = RobotNASCandidate(config)
            self.population.append(candidate)
    
    def generate_random_architecture(self):
        """Generate a random architecture within search space"""
        layers = []
        
        # Generate random sequence of layers
        num_layers = np.random.randint(3, 8)  # 3-8 layers
        
        for _ in range(num_layers):
            layer_type = np.random.choice(['conv', 'linear', 'residual'])
            layer_config = self.sample_layer_configuration(layer_type)
            layers.append(layer_config)
        
        return layers
    
    def sample_layer_configuration(self, layer_type):
        """Sample configuration for a layer type"""
        if layer_type == 'conv':
            return {
                'type': 'conv',
                'in_channels': np.random.choice([16, 32, 64, 128]),
                'out_channels': np.random.choice([32, 64, 128, 256]),
                'kernel_size': np.random.choice([3, 5, 7])
            }
        elif layer_type == 'linear':
            return {
                'type': 'linear',
                'in_size': np.random.choice([64, 128, 256, 512]),
                'out_size': np.random.choice([32, 64, 128, 256])
            }
        elif layer_type == 'residual':
            return {
                'type': 'residual',
                'channels': np.random.choice([64, 128, 256])
            }
    
    def evaluate_candidate(self, candidate, eval_env):
        """Evaluate a candidate architecture"""
        try:
            network = candidate.build_network()
            
            # Test accuracy on evaluation environment
            accuracy = self.evaluate_accuracy(network, eval_env)
            
            # Estimate computational cost
            latency = self.estimate_latency(network)
            power = self.estimate_power_usage(network)
            
            # Combined fitness score
            fitness = (self.objectives['accuracy'] * accuracy - 
                      self.objectives['latency'] * latency - 
                      self.objectives['power_efficiency'] * power)
            
            candidate.fitness_score = fitness
            candidate.computation_cost = latency
            
            return fitness
            
        except Exception as e:
            # Penalize invalid architectures
            candidate.fitness_score = -1.0
            return -1.0
    
    def evolve_population(self):
        """Evolve population using genetic operators"""
        # Sort by fitness
        self.population.sort(key=lambda x: x.fitness_score, reverse=True)
        
        # Keep top performers
        survivors = self.population[:int(0.2 * self.population_size)]
        
        # Generate offspring through mutation and crossover
        offspring = []
        for _ in range(self.population_size - len(survivors)):
            parent1 = np.random.choice(survivors)
            if np.random.rand() < 0.8:  # Crossover 80% of the time
                parent2 = np.random.choice(survivors)
                child_config = self.crossover(parent1.layers_config, parent2.layers_config)
            else:  # Mutation otherwise
                child_config = self.mutate(parent1.layers_config)
            
            offspring.append(RobotNASCandidate(child_config))
        
        self.population = survivors + offspring
    
    def crossover(self, config1, config2):
        """Combine two architectures"""
        # Simplified crossover: take half from each
        mid_point = len(config1) // 2
        child_config = config1[:mid_point] + config2[mid_point:]
        return child_config
    
    def mutate(self, config):
        """Mutate an architecture"""
        mutated_config = config.copy()
        # Randomly modify ~20% of layers
        for i in range(len(mutated_config)):
            if np.random.rand() < 0.2:
                mutated_config[i] = self.sample_layer_configuration(
                    mutated_config[i]['type'])
        return mutated_config
    
    def search(self, eval_env):
        """Run the entire NAS process"""
        self.initialize_population()
        
        for generation in range(self.generations):
            print(f"Evaluating generation {generation + 1}/{self.generations}")
            
            for candidate in self.population:
                self.evaluate_candidate(candidate, eval_env)
            
            # Report best from generation
            best = max(self.population, key=lambda x: x.fitness_score)
            print(f"Best fitness: {best.fitness_score:.4f}")
            
            # Evolve for next generation
            self.evolve_population()
        
        # Return best architecture
        best_final = max(self.population, key=lambda x: x.fitness_score)
        return best_final

Summary

This chapter covered advanced AI techniques for humanoid robot brains:

• Reinforcement learning for locomotion and control
• Isaac ROS integration for perception-action systems
• Adaptive control systems that adjust in real-time
• Hierarchical decision-making for complex tasks
• Sim-to-real transfer techniques including domain randomization
• Curriculum learning for gradual skill acquisition
• Neural architecture search for optimizing robot neural networks

These techniques enable humanoid robots to learn complex behaviors, adapt to changing conditions, and execute sophisticated tasks that require both perception and action capabilities.

Exercises

1. Implement a simple DDPG agent for basic humanoid control
2. Create a domain randomization scheme for your simulation environment
3. Design a hierarchical task planner for a specific humanoid task

Next Steps

With advanced AI techniques for humanoid robots covered, the next module will focus on Vision-Language-Action systems that integrate perception, cognition, and action in unified frameworks for natural human-robot interaction.