Autonomous Robotics, Task Planning & Agentic Execution

For the full course overview and capstone description, see the Physical AI & Humanoid Robotics — Course Specification.

Chapter Overview

Duration: Weeks 14–18
Focus: Decision-making, navigation, policy execution, and self-operated task autonomy

Chapter 5 elevates your humanoid from a perceptive, controllable system to a full autonomous agent. Building on ROS 2 (Chapter 2), digital twins (Chapter 3), and perception & mapping (Chapter 4), this chapter introduces navigation stacks, global and local planning, behavior trees, skill libraries, and LLM-guided decision-making. You will learn how to turn high-level instructions into executable task graphs and policies that drive navigation and manipulation in real environments.

By the end of this chapter, your robot will be able to receive a natural-language instruction, plan, navigate, manipulate objects, recover from failures, and complete tasks with minimal human intervention. This is where the system stops being “just a robot” and becomes an AI agent.

Learning Outcomes

Conceptual Understanding

• Understand what autonomy means and how it differentiates robots from remote tools
• Learn task-planning pipelines and agentic execution models
• Understand navigation stacks, global vs local planners, and command hierarchies
• Grasp hierarchical control: high-level goals → policies → motor execution
• Study decision-making architectures that combine LLM reasoning with reinforcement learning and classical planning
• Comprehend failure handling, fallback states, and self-recovery strategies

Practical Skills

• Build waypoint-based navigation and room traversal with ROS 2 navigation stacks
• Execute autonomous pick, place, follow, deliver, and inspect tasks
• Use behavior trees, task graphs, and goal chains to structure robot behaviors
• Integrate LLM-based reasoning with continuous sensor feedback loops
• Implement natural-language command execution that maps to structured robot tasks
• Deploy an end-to-end autonomous agent pipeline in simulation and, where available, on hardware

Final Goal Alignment

• The robot can receive an instruction → plan → act → verify without manual teleop
• All core system layers converge: perception, planning, control, and reasoning
• Provides the foundation for multi-agent collaboration and fleet orchestration in a future chapter

Chapter Structure

This chapter is organized into five topics/modules:

Topic 1: Foundations of Autonomy & Agent-Based Robotics (Week 14)

Defines autonomy, agent architectures, and the role of state awareness, perception, planning, and execution. Introduces the concept of an “agent loop” that continuously senses, thinks, and acts.

Topic 2: Planning & Navigation Systems (Weeks 14–15)

Covers navigation stacks (e.g., ROS 2 Nav2), global vs local planning, and waypoint missions. You will build room-to-room traversal on SLAM maps and script navigation missions with ROS 2 actions.

Topic 3: Task Execution & Action Sequencing (Weeks 15–16)

Introduces task graphs and behavior trees, skill libraries (pick, place, follow, deliver), and how to chain skills into complete tasks. Focuses on robust execution with retries and failure handling.

Topic 4: LLM-Based Decision Making & Reasoning (Weeks 16–17)

Shows how to translate natural-language instructions into structured task graphs, integrate LLMs into closed-loop autonomy, and use reinforcement-style feedback to refine skills.

Topic 5: Embodied Action — Manipulation & Environment Interaction (Weeks 17–18)

Connects high-level tasks to precise manipulation and human interaction. Covers grasping and precision control, object delivery, and human-centered tasks like handovers and escorting.

The chapter culminates in a Full Autonomous Task Demo milestone: an end-to-end scenario where the robot receives a natural-language task and completes it autonomously.

Prerequisites

Before starting this chapter, you should have:

• Completed Chapters 1–4:
  • Foundations of Physical AI
  • ROS 2 Middleware and multi-node systems
  • Digital twins and simulation (Gazebo/Isaac Sim)
  • Perception, mapping, and multimodal understanding
• A working navigation-ready digital twin:
  • Simulated humanoid with cameras, IMU, and (optionally) LiDAR
  • SLAM-based mapping available from Chapter 4
• Comfort with:
  • Writing and launching ROS 2 nodes in Python/C++
  • Basic linear algebra and 3D transforms
  • Reading and reasoning about behavior trees or state machines

Hardware access (real robot) is helpful but not required; all concepts can be exercised in simulation first.

Technical Requirements

Software Stack

• ROS 2 Humble or Iron (Ubuntu 22.04 LTS)
• ROS 2 navigation stack (Nav2 or equivalent)
• Behavior tree or task-graph library (e.g., BehaviorTree.CPP)
• SLAM and mapping from Chapter 4 (e.g., VSLAM or LiDAR SLAM)
• LLM/VLM access (local or remote) for high-level reasoning
• Gazebo or Isaac Sim (from Chapter 3) for safe navigation and task testing

Hardware

• Linux workstation with GPU for perception and LLM/VLM inference (or cloud access)
• For hardware deployment:
  • Humanoid or mobile base with:
    • RGB-D camera
    • IMU
    • Motorized base and manipulators
  • Network connectivity between robot and workstation

External Dependencies

• nav2 and associated planners/controllers
• Behavior tree tooling (e.g., BehaviorTree.CPP, Nav2 BT XML configuration)
• Optional reinforcement learning frameworks (for skill tuning)
• Access to LLM APIs or on-prem models for decision-making and task graph generation

Reading Materials

Primary Resources

• ROS 2 Nav2 documentation and tutorials
• Behavior tree documentation and examples (e.g., BehaviorTree.CPP)
• ROS 2 Action and lifecycle tutorials

Secondary Resources

• Research papers on task and motion planning for mobile manipulators
• Articles on integrating LLMs with robotics for task planning and grounding
• Tutorials on hierarchical RL and skill-based control

Reference Materials

• Nav2 configuration and tuning guides
• ROS 2 action and behavior tree examples for navigation and manipulation
• Example task graphs and behavior trees for mobile robots

Common Mistakes to Avoid

Mistake: Treating autonomy as a “single node” problem. Result: Monolithic agents that are hard to debug and extend.
Prevention: Maintain a clear separation between perception, planning, control, and reasoning; use well-defined ROS 2 interfaces and behavior trees/task graphs.

Mistake: Ignoring failure modes and edge cases. Result: Robot freezes or behaves unpredictably when something goes wrong.
Prevention: Design explicit failure states, retries, and safe fallbacks in task graphs and behavior trees.

Mistake: Over-trusting LLM outputs without grounding. Result: Plans that are semantically correct but physically impossible.
Prevention: Use LLMs to propose or refine task graphs, but validate them against maps, kinematics, and safety constraints before execution.

Mistake: Hard-coding navigation missions. Result: Brittle systems that break when the environment changes.
Prevention: Build waypoint and room traversal on top of SLAM maps and dynamic planners, not fixed coordinates.

Mistake: Ignoring timing and resource constraints. Result: Autonomy loops that are too slow or overload hardware.
Prevention: Measure latency across perception–planning–control, use appropriate action horizons, and offload heavy LLM calls when necessary.

Chapter Summary

Duration: 5 weeks (Weeks 14–18)
Topics/Modules: 5
Major Milestone: Full Autonomous Task Demo
Total Estimated Reading: 120–160 pages
Total Estimated Coding: 40–60 hours

Key Takeaways

• Autonomy emerges when perception, planning, control, and reasoning are integrated into a closed-loop agent
• Navigation stacks (maps, localization, planners, controllers) are the backbone of autonomous movement
• Behavior trees and task graphs provide structure, robustness, and debuggability for complex tasks
• LLMs can augment decision-making but must be grounded in real sensor data, maps, and safety constraints
• Robust autonomy depends on explicit handling of failures, fallbacks, and continuous feedback

Next Chapter Prerequisites

By the end of Chapter 5, you should have:

• A functioning autonomy stack that can:
  • Receive high-level (possibly natural language) tasks
  • Plan paths and sequences of skills
  • Execute navigation and manipulation behaviors with feedback
• Behavior trees or task graphs that encode at least a few realistic humanoid tasks
• Experience integrating LLM-based reasoning into closed-loop robotic control

These capabilities position your humanoid for future extensions such as multi-agent coordination, fleet management, and large-scale deployments.