cognitive/docs/guides/learning_paths/active_inference_robotics_learning_path.md

title	type	status	created	complexity	processing_priority	tags	semantic_relations
Active Inference in Robotics Learning Path	learning_path	stable	2024-03-15	advanced	1	active-inference robotics control-theory autonomous-systems	type links specializes active_inference_learning_path type links relates robotics_learning_path control_theory_learning_path autonomous_systems_learning_path

Active Inference in Robotics Learning Path

Overview

This specialized path focuses on applying Active Inference to robotics and autonomous systems, integrating perception, control, and learning for robust robotic behavior.

Prerequisites

1. Robotics Foundations (4 weeks)

• Robot Kinematics

  • Forward kinematics
  • Inverse kinematics
  • Jacobians
  • Dynamics

• Control Theory

  • State space control
  • Feedback control
  • Optimal control
  • Adaptive control

• Perception Systems

  • Sensor processing
  • Computer vision
  • State estimation
  • Sensor fusion

• Planning and Navigation

  • Path planning
  • Motion planning
  • SLAM
  • Obstacle avoidance

2. Technical Skills (2 weeks)

• Programming Tools
  • Python/C++
  • ROS/ROS2
  • Simulation environments
  • Hardware interfaces

Core Learning Path

1. Robot Implementation (4 weeks)

Week 1-2: Robot State Estimation

class RobotStateEstimator:
    def __init__(self,
                 state_dim: int,
                 sensor_dim: int):
        """Initialize robot state estimator."""
        self.state_model = StateTransitionModel(state_dim)
        self.sensor_model = SensorModel(state_dim, sensor_dim)
        self.state = torch.zeros(state_dim)
        
    def estimate_state(self,
                      sensor_data: torch.Tensor,
                      action: torch.Tensor) -> torch.Tensor:
        """Estimate robot state from sensor data."""
        # Prediction step
        state_pred = self.state_model(self.state, action)
        
        # Update step
        sensor_pred = self.sensor_model(state_pred)
        error = sensor_data - sensor_pred
        
        # State correction
        self.state = state_pred + self.compute_update(error)
        return self.state

Week 3-4: Action Generation

class ActiveInferenceController:
    def __init__(self,
                 action_dim: int,
                 goal_dim: int):
        """Initialize active inference controller."""
        self.policy_network = PolicyNetwork(action_dim)
        self.goal_prior = GoalPrior(goal_dim)
        
    def select_action(self,
                     current_state: torch.Tensor,
                     goal_state: torch.Tensor) -> torch.Tensor:
        """Select action using active inference."""
        # Compute expected free energy for policies
        policies = self.policy_network.generate_policies()
        G = torch.zeros(len(policies))
        
        for i, policy in enumerate(policies):
            # Simulate policy
            predicted_states = self.simulate_policy(current_state, policy)
            # Compute expected free energy
            G[i] = self.compute_expected_free_energy(
                predicted_states, goal_state
            )
        
        # Select policy with lowest expected free energy
        best_policy = policies[torch.argmin(G)]
        return best_policy[0]  # Return first action

2. Robot Systems (6 weeks)

Week 1-2: Perception Systems

• Visual Processing
• Tactile Sensing
• Force Sensing
• Multimodal Integration

Week 3-4: Control Systems

• Motor Control
• Force Control
• Impedance Control
• Whole-body Control

Week 5-6: Learning Systems

• Skill Learning
• Task Learning
• Adaptation
• Transfer Learning

3. Applications (4 weeks)

Week 1-2: Manipulation Tasks

class ManipulationTask:
    def __init__(self,
                 robot: Robot,
                 environment: Environment):
        """Initialize manipulation task."""
        self.robot = robot
        self.env = environment
        self.planner = TaskPlanner()
        
    def execute_task(self,
                    task_spec: Dict[str, Any]) -> bool:
        """Execute manipulation task."""
        # Generate task plan
        plan = self.planner.plan_task(task_spec)
        
        # Execute actions
        for action in plan:
            success = self.robot.execute_action(action)
            if not success:
                return self.handle_failure(action)
        
        return self.verify_task_completion(task_spec)

Week 3-4: Navigation Tasks

• Path Planning
• Obstacle Avoidance
• SLAM
• Multi-robot Coordination

4. Advanced Topics (4 weeks)

Week 1-2: Human-Robot Interaction

class HumanRobotInteraction:
    def __init__(self,
                 robot: Robot,
                 human_model: HumanModel):
        """Initialize human-robot interaction."""
        self.robot = robot
        self.human = human_model
        self.interaction_model = InteractionModel()
        
    def adapt_behavior(self,
                      human_state: torch.Tensor) -> torch.Tensor:
        """Adapt robot behavior to human."""
        # Infer human intention
        intention = self.human.infer_intention(human_state)
        
        # Generate adaptive behavior
        robot_action = self.interaction_model.generate_action(
            self.robot.state, intention
        )
        
        return robot_action

Week 3-4: Learning and Adaptation

• Online Learning
• Adaptive Control
• Robust Behavior
• Safety Constraints

Projects

Manipulation Projects

1. Object Manipulation

   • Grasping
   • Assembly
   • Tool Use
   • Dexterous Manipulation

2. Task Learning

   • Skill Acquisition
   • Task Adaptation
   • Failure Recovery
   • Generalization

Navigation Projects

1. Mobile Robotics

   • Indoor Navigation
   • Outdoor Exploration
   • Dynamic Environments
   • Multi-robot Systems

2. Interactive Tasks

   • Human Collaboration
   • Social Navigation
   • Gesture Recognition
   • Natural Interfaces

Assessment

Technical Assessment

1. System Implementation

   • Perception Systems
   • Control Systems
   • Learning Systems
   • Integration

2. Performance Evaluation

   • Task Success
   • Robustness
   • Efficiency
   • Safety

Final Projects

1. Research Project

   • Novel Algorithm
   • System Integration
   • Experimental Validation
   • Documentation

2. Applied Project

   • Real Robot Implementation
   • Task Demonstration
   • Performance Analysis
   • User Study

Resources

Technical Resources

1. Software Tools

   • ROS/ROS2
   • Simulation Environments
   • Control Libraries
   • Vision Libraries

2. Hardware Platforms

   • Robot Arms
   • Mobile Platforms
   • Sensors
   • Computing Systems

Learning Resources

1. Documentation

   • API References
   • Tutorials
   • Example Code
   • Best Practices

2. Research Papers

   • Core Methods
   • Applications
   • Case Studies
   • Benchmarks

Next Steps

Advanced Topics

1. advanced_robotics_learning_path
2. human_robot_interaction_learning_path
3. robot_learning_learning_path

Research Directions

1. research_guides/robotics
2. research_guides/control_theory
3. research_guides/autonomous_systems