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Chapter 5 — Autonomous Robotics, Task Planning & Agentic Execution

Overview​

By Chapter 4, your humanoid can perceive and understand its environment. Chapter 5 turns this perceptual capability into autonomous behavior. You will design navigation stacks, build task-level controllers, integrate large language models (LLMs) for high-level reasoning, and connect everything into an agentic control loop that can execute tasks end-to-end.

In this chapter, you will move from "the robot can see and move" to "the robot can decide what to do and how to do it". You will combine mapping, perception, planning, and control into a coherent system where the robot:

  • Accepts high-level natural language commands.
  • Plans paths through environments.
  • Executes skills like pick, place, follow, and deliver.
  • Recovers from failures and unexpected events.

Duration: Weeks 14–18
Focus: Decision-making, navigation, hierarchical control, and agentic task execution

Learning Objectives​

Conceptual Understanding​

  • Understand what makes a robot autonomous rather than remotely operated.
  • Learn end-to-end task-planning pipelines and agentic execution models.
  • Distinguish between global and local planning in navigation stacks.
  • Understand hierarchical control: high-level goals → task graphs/behavior trees → skills → motor commands.
  • Study how LLMs and reinforcement learning strategies can support decision-making and refinement.
  • Comprehend failure handling, fallback states, and self-recovery behaviors.

Practical Skills​

  • Build waypoint-based navigation and multi-room traversal using ROS 2 Nav2 or similar stacks.
  • Implement autonomous tasks: pick, place, follow, deliver, and inspect.
  • Design and implement behavior trees or task graphs to sequence skills and handle failures.
  • Integrate an LLM or VLM with continuous sensor feedback for closed-loop autonomy.
  • Implement natural-language command execution that triggers navigation and manipulation.
  • Deploy an end-to-end autonomous agent pipeline in both simulation (digital twin) and, where possible, hardware.

Final Goal Alignment​

  • The robot can receive a high-level instruction → interpret it → plan → act without human teleoperation.
  • All core system layers converge: perception, mapping, planning, control, and language reasoning.
  • Establishes the foundation for Chapter 6 (multi-robot collaboration and fleet orchestration, if pursued).

Chapter Structure​

Chapter 5 is organized around five topics that layer autonomy on top of perception and control:

Topic 1: Foundations of Autonomy & Agent-Based Robotics​

  • What makes a robot autonomous: state awareness, perception, planning, and execution.
  • System architecture of an autonomous agent:
    • LLM/VLM for high-level reasoning.
    • Planner for decision execution.
    • Controllers for actuators and motor commands.
    • Feedback loops for continuous reevaluation.

Topic 2: Planning & Navigation Systems​

  • Navigation stacks (e.g., ROS 2 Nav2) and their components:
    • Map + localization + global planner + local planner + controller.
  • Global vs local planning, dynamic replanning with real-time sensor input.
  • Waypoint missions for room-to-room traversal using SLAM maps and ROS actions.

Topic 3: Task Execution & Action Sequencing​

  • Task graphs and behavior trees for structured decision-making.
  • Skill libraries for common humanoid tasks: pick, place, follow, deliver, inspect.
  • Chaining skills into full tasks (e.g., find object → navigate → grasp → deliver).

Topic 4: LLM-Based Decision Making & Reasoning​

  • Translating natural language into structured task graphs and goals.
  • Closed-loop autonomy: perception-informed decisions, clarification requests, and self-correction.
  • Reinforcement- and feedback-based task optimization and logging for continual improvement.

Topic 5: Embodied Action, Manipulation & Capstone Integration​

  • Grasping and precision control: IK, grip-force regulation, slippage detection.
  • Object placement and delivery, human interaction tasks (hand-over, escort).
  • Capstone milestone: full autonomous task demo with natural-language input.

Use the sidebar to navigate into each topic for detailed explanations, examples, and labs.

Reading Materials​

Primary Resources​

  • ROS 2 Navigation (Nav2) Documentation — Architecture, planners, behavior trees, configuration.
  • Behavior Trees in Robotics (papers and tutorials) — Design patterns for task-level control.
  • Task and Motion Planning (TAMP) survey articles — Integrating symbolic planning and motion planning.
  • LLM-based Robotics (e.g., VLA/VLM papers) — Using language models for high-level policy selection.

Secondary Resources​

  • Reinforcement Learning: An Introduction — For understanding reward design and policy optimization.
  • Case studies of autonomous mobile robots and humanoids (e.g., Boston Dynamics, Tesla, Toyota Research).

Reference​

  • ROS 2 action interfaces and behavior-tree configuration files.
  • Nav2 tutorials for custom behavior trees and planners.
  • Example open-source behavior-tree frameworks for robotics.

Technical Requirements​

Software Stack​

  • ROS 2 Humble or Iron (Ubuntu 22.04 LTS).
  • Nav2 or equivalent navigation stack (global/local planners, behavior tree executor).
  • Behavior tree / task-graph library (e.g., BehaviorTree.CPP or similar).
  • Inverse kinematics and control libraries for manipulation.
  • LLM/VLM API or local runtime for high-level reasoning (optional but strongly recommended).

Hardware​

  • Same base hardware as previous chapters:
    • GPU-capable workstation (for simulation and perception).
    • Edge compute platform (e.g., Jetson) for on-robot deployment.
  • Access to:
    • A simulated humanoid in Gazebo/Isaac Sim.
    • Optional physical platform (e.g., Unitree humanoid) for final demos.

External Dependencies​

  • Nav2 packages and dependencies (nav2_bringup, planners, controllers).
  • Inverse kinematics/trajectory planning software (e.g., MoveIt or custom).
  • LLM/VLM integration libraries or SDKs.

Key Takeaways​

By the end of this chapter, you should be able to:

  • Architect and implement a navigation stack for humanoid robots.
  • Design task graphs and behavior trees that chain skills into robust tasks.
  • Integrate LLM-based reasoning with perception and planning for natural-language control.
  • Handle failures and unexpected conditions through well-designed fallback and recovery behaviors.
  • Demonstrate an end-to-end autonomous agent that can receive tasks, plan, and act without continuous human supervision.

Next Chapter Prerequisites​

Before moving to any advanced topics (e.g., multi-agent systems or fleet orchestration), ensure you have:

  • ✅ A functioning navigation stack (global + local planners + controller) in simulation.
  • ✅ At least one task graph or behavior tree that can execute multi-step tasks reliably.
  • ✅ A small library of tested skills (pick, place, follow, deliver) integrated with your humanoid.
  • ✅ A natural-language interface that can trigger tasks through structured representations.
  • ✅ Logs and metrics for navigation success rates, task completion rates, and failure modes.

With these pieces in place, your humanoid is no longer just a controlled robot—it is a physical AI agent capable of autonomous operation.