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Chapter 04: Agent Architectures

Overview​

This chapter explores how to design AI agent architectures that can control robots effectively. It covers different agent paradigms, from reactive agents to deliberative planners, and how large models can enhance agent capabilities for robotic control.

Learning Objectives​

  • Understand agent architectures for robotics
  • Learn reactive vs deliberative agents
  • Explore hierarchical agent designs
  • Master LLM-based agent planning
  • Understand agent learning frameworks

Core Concepts​

1. Agent Architecture Types​

Reactive Agents:

  • Direct sensor-to-action mapping
  • Fast response times
  • Limited reasoning capability

Deliberative Agents:

  • Internal world model
  • Planning before action
  • Slower but more intelligent

Hybrid Agents:

  • Combine reactive and deliberative
  • Fast reactions + deep planning
  • Best for complex robots

2. Hierarchical Agent Design​

Three-Layer Architecture:

High-Level (LLM) → Task Planning
Mid-Level (Controller) → Action Sequences  
Low-Level (Actuators) → Motor Commands

Benefits:

  • Separation of concerns
  • Modular design
  • Easier debugging
  • Scalable complexity

3. LLM-Based Agent Planning​

Planning Process:

  1. Parse natural language task
  2. Generate action sequence
  3. Verify feasibility
  4. Execute with monitoring
  5. Adapt based on feedback

Example:

Task: "Clean the kitchen"
→ Break into: [Find dishes, Pick up dishes, Wash dishes, Put away dishes]
→ Each step further decomposed
→ Execute with safety checks

4. Agent Learning Frameworks​

Reinforcement Learning Agents:

  • Learn from trial and error
  • Reward-based optimization
  • Policy gradient methods

Imitation Learning Agents:

  • Learn from demonstrations
  • Behavior cloning
  • Fewer samples needed

Hybrid Learning:

  • Combine RL and IL
  • Faster convergence
  • Better generalization

5. Multi-Agent Systems​

Coordination:

  • Communication protocols
  • Shared world models
  • Distributed planning
  • Conflict resolution

Technical Deep Dive​

Agent Architecture Example:

class RobotAgent:
    def __init__(self):
        self.llm_planner = LLMPlanner()
        self.controller = MotionController()
        self.executor = ActionExecutor()
    
    def execute_task(self, task_description):
        plan = self.llm_planner.plan(task_description)
        for action in plan:
            trajectory = self.controller.generate(action)
            self.executor.execute(trajectory)

Real-World Application​

A warehouse robot agent that:

  • Receives high-level instructions
  • Plans optimal paths
  • Coordinates with other robots
  • Adapts to dynamic environments
  • Reports status and issues

Hands-On Exercise​

Exercise: Design an agent architecture for a humanoid robot that can:

  • Understand natural language commands
  • Plan multi-step tasks
  • Handle unexpected situations
  • Learn from experience

Summary​

Agent architectures enable:

  • Intelligent task execution
  • Natural language understanding
  • Complex planning capabilities
  • Adaptive behavior
  • Scalable robot intelligence

References​

  • Agent Architectures in Robotics
  • LLM-Based Planning Systems
  • Hierarchical Control for Robots