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Chapter 12: LLM-Based Planning for Humanoid Robots

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Fill in content from specs/001-physical-ai-book-specs/spec.md lines 1095-1168. Use Chapter 11 (11-voice-to-action.mdx) as formatting reference.

Overview​

TODO: Add overview paragraph introducing LLMs for task planning (spec.md lines 1097-1099)

Target Audience: Developers integrating AI planning with robotic systems.

Learning Objectives​

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

  1. TODO: Add learning objective 1 from spec.md line 1103
  2. TODO: Add learning objective 2 from spec.md line 1104
  3. TODO: Add learning objective 3 from spec.md line 1105
  4. TODO: Add learning objective 4 from spec.md line 1106
  5. TODO: Add learning objective 5 from spec.md line 1107

LLM-Based Planning Overview​

TODO: Explain LLM role in robotics (spec.md line 1111)

  • Task decomposition
  • Reasoning
  • Code generation

Mermaid Diagram TODO: Create LLM Planning Pipeline diagram (spec.md line 1143)

TODO: Natural Language Goal → LLM API → Action Sequence → ROS 2 Action Servers → Robot

Prompting Strategies for Robot Planning​

TODO: Explain prompting techniques (spec.md line 1112)

  • Few-shot examples
  • Chain-of-thought
  • Structured outputs

Mermaid Diagram TODO: Create Prompt Structure diagram (spec.md line 1146)

TODO: System prompt + Few-shot examples + User task → LLM output (JSON action sequence)

Integrating LLM APIs with ROS 2​

TODO: Add LLM-ROS 2 integration section (spec.md line 1113)

TODO: Add installation and API setup commands (spec.md lines 1125-1139)

# TODO: Add commands from spec.md lines 1128-1138
# - Install OpenAI client
# - Call LLM for task planning
# - Execute generated action sequence
# - Monitor execution status

Task Decomposition with LLMs​

TODO: Explain task decomposition (spec.md line 1114)

Mermaid Diagram TODO: Create Task Decomposition diagram (spec.md line 1144)

TODO: High-level task → Sub-tasks → Primitive actions (navigate, pick, place)

Example TODO: Add "Set the table for dinner" example (spec.md line 1120)

Parsing and Executing LLM Outputs​

TODO: Add section on parsing LLM outputs (spec.md line 1106)

  • JSON action sequences
  • Validation
  • Execution via ROS 2

Example TODO: Add structured output example (spec.md line 1121)

Error Recovery and Re-Planning​

TODO: Explain error recovery with LLMs (spec.md line 1115)

Mermaid Diagram TODO: Create Error Recovery Flow (spec.md line 1145)

TODO: Action fails → LLM re-plans → Execute new plan

Example TODO: Add re-planning example (spec.md line 1122)

LLM Code Generation for Robot Tasks​

TODO: Add section on code generation (spec.md line 1123)

  • Generate Python code for ROS 2 APIs
  • Orchestrate multi-step tasks

Practice Tasks​

Complete these exercises to master LLM-based planning:

Task 1: Setup LLM API​

TODO: Add task details from spec.md line 1150

Task 2: Create Planning Node​

TODO: Add task details from spec.md line 1151

Task 3: Parse and Execute Actions​

TODO: Add task details from spec.md line 1152

Task 4: Implement Error Recovery​

TODO: Add task details from spec.md line 1153

Summary​

TODO: Add summary points from spec.md lines 1157-1162:

  • LLMs for task planning and reasoning
  • Task decomposition
  • Prompting strategies
  • Parsing and execution
  • Error recovery
  • Use cases

References​

Next Chapter: Chapter 13: Multimodal Robotics - Integrate vision, language, and action into unified VLA systems.