---
title: Active Inference in Robotics Learning Path
type: learning_path
status: stable
created: 2024-03-15
complexity: advanced
processing_priority: 1
tags:
  - active-inference
  - robotics
  - control-theory
  - autonomous-systems
semantic_relations:
  - type: specializes
    links: [[active_inference_learning_path]]
  - type: relates
    links:
      - [[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
```python
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
```python
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
```python
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
```python
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|Advanced Robotics]]
2. [[human_robot_interaction_learning_path|Human-Robot Interaction]]
3. [[robot_learning_learning_path|Robot Learning]]

### Research Directions
1. [[research_guides/robotics|Robotics Research]]
2. [[research_guides/control_theory|Control Theory Research]]
3. [[research_guides/autonomous_systems|Autonomous Systems Research]]