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Cognitive Planning - LLMs Translating Natural Language to ROS 2 Actions

Learning Objectives​

  • Understand cognitive planning in the context of natural language to action translation
  • Implement Large Language Models (LLMs) for robotic task planning
  • Design interfaces between LLMs and ROS 2 action servers
  • Create systems that decompose complex language commands into executable actions
  • Implement context-aware planning with environmental awareness

Overview​

Cognitive planning bridges the gap between high-level natural language commands and low-level robotic actions. This involves using Large Language Models (LLMs) to understand user intentions, consider environmental context, and generate detailed action sequences that can be executed by robotic systems through ROS 2. The planning process must account for robot capabilities, environmental constraints, and task requirements.

Cognitive Planning Architecture​

System Components​

  1. Language Understanding: Interpreting natural language commands
  2. World Modeling: Maintaining representation of the environment
  3. Task Planning: Decomposing high-level tasks into executable actions
  4. Action Execution: Executing planned actions through ROS 2
  5. Feedback Integration: Updating plans based on execution results

Planning Hierarchy​

  • High-Level Planning: Overall task decomposition and strategy
  • Mid-Level Planning: Specific action sequences and resource allocation
  • Low-Level Planning: Detailed motion planning and control

LLM Integration for Planning​

LLM Selection Criteria​

  • Reasoning Capabilities: Ability to decompose complex tasks
  • Knowledge Integration: Access to world knowledge and commonsense reasoning
  • Context Handling: Understanding of environmental and situational context
  • Action Generation: Ability to generate executable action sequences
  • OpenAI GPT Models: Strong reasoning and language understanding
  • Anthropic Claude: Excellent instruction following and safety
  • Open Source Models: Llama, Mistral for local deployment
  • Specialized Models: Models fine-tuned for robotics tasks

Natural Language to Action Translation​

Command Decomposition​

Complex commands must be broken down into simpler, executable steps:

Command: "Please bring me the red coffee cup from the kitchen table"
Decomposition:
1. Navigate to kitchen
2. Identify red coffee cup on table
3. Plan grasp for the cup
4. Execute grasp
5. Navigate back to user
6. Present cup to user

Action Representation​

  • Symbolic Actions: High-level action descriptions
  • Parameterized Actions: Actions with specific parameters
  • Conditional Actions: Actions that depend on environmental conditions
  • Temporal Actions: Actions with timing constraints

ROS 2 Integration Patterns​

Planning Service Architecture​

  • Plan Generation Service: Takes high-level commands and returns action sequences
  • Plan Validation Service: Checks feasibility of generated plans
  • Plan Execution Service: Executes action sequences with monitoring
  • Plan Adaptation Service: Modifies plans based on execution feedback

Message Types for Planning​

  • PlanRequest: High-level command and context
  • PlanResponse: Sequence of actions with parameters
  • PlanStatus: Execution status and feedback
  • PlanUpdate: Modifications to ongoing plans

Context-Aware Planning​

Environmental Context​

  • Object Locations: Current positions of relevant objects
  • Robot State: Current position, battery level, capabilities
  • Human Context: User location, preferences, activity
  • Temporal Context: Time of day, day of week, schedule

Knowledge Integration​

  • Commonsense Knowledge: Understanding of typical object affordances
  • Spatial Reasoning: Understanding of spatial relationships
  • Social Conventions: Understanding of appropriate robot behavior
  • Task Knowledge: Understanding of specific task requirements

Planning Algorithms​

Symbolic Planning​

  • STRIPS: Classical planning with state representations
  • PDDL: Planning Domain Definition Language for complex planning
  • Hierarchical Task Networks: Task decomposition with methods
  • Automated Planning: Automated generation of action sequences

Learning-Based Planning​

  • Neural Planning: Neural networks for action selection
  • Reinforcement Learning: Learning optimal planning strategies
  • Imitation Learning: Learning from human demonstrations
  • Language-Guided Planning: Using language to guide planning

Implementation Approaches​

Centralized Planning​

  • Single Planning Node: One node handles all planning decisions
  • Complete World Model: Centralized representation of environment
  • Coordinated Execution: Coordinated execution of all robot actions
  • Simplified Coordination: Easy coordination between different subsystems

Distributed Planning​

  • Modular Planning: Different planners for different task types
  • Decentralized Knowledge: Distributed representation of world knowledge
  • Parallel Execution: Parallel execution of independent action sequences
  • Robust to Failures: Continued operation despite partial failures

Safety and Validation​

Plan Validation​

  • Feasibility Checking: Verify plans are physically possible
  • Safety Checking: Ensure plans don't cause harm
  • Resource Checking: Verify sufficient resources for plan execution
  • Temporal Checking: Verify timing constraints are met

Safety Mechanisms​

  • Emergency Stop: Immediate halt for safety-critical situations
  • Plan Monitoring: Continuous monitoring of plan execution
  • Fallback Behaviors: Safe behaviors when plans fail
  • Human Intervention: Mechanisms for human override

Context and Memory Management​

Short-term Memory​

  • Task Context: Current task and subtask information
  • Execution History: Recent actions and their outcomes
  • Environmental Changes: Recent changes in the environment
  • User Preferences: Current user preferences and requests

Long-term Memory​

  • Learned Behaviors: Previously successful action sequences
  • Object Knowledge: Information about object properties and locations
  • User Profiles: Long-term user preferences and interaction history
  • Environmental Maps: Persistent environmental information

Performance Considerations​

Latency Optimization​

  • Caching: Cache frequently used plans and knowledge
  • Pre-computation: Pre-compute common planning scenarios
  • Parallel Processing: Parallelize planning and execution where possible
  • Approximate Planning: Use faster approximate methods when appropriate

Resource Management​

  • Model Loading: Efficient loading and unloading of LLMs
  • Memory Management: Efficient use of memory for planning
  • Computation Distribution: Distribute planning across available resources
  • Power Management: Consider power consumption in planning decisions

Integration with Previous Modules​

Leveraging ROS 2 Infrastructure (Module 1)​

  • Use established communication patterns for planning services
  • Integrate with existing node structures and message types
  • Leverage ROS 2 tools for monitoring and debugging

Simulation-Based Validation (Module 2)​

  • Test planning algorithms in simulated environments
  • Validate safety mechanisms in safe simulation environments
  • Generate training data for learning-based planning approaches

AI Perception Integration (Module 3)​

  • Use perception data to inform planning decisions
  • Integrate with Isaac ROS for advanced perception
  • Combine visual and linguistic information for planning

Error Handling and Recovery​

Planning Errors​

  • Infeasible Plans: Plans that cannot be executed
  • Incomplete Information: Insufficient information for planning
  • Contradictory Commands: Conflicting or impossible commands
  • Resource Limitations: Insufficient resources for requested tasks

Recovery Strategies​

  • Plan Repair: Modify plans to address identified issues
  • Information Gathering: Request additional information when needed
  • Alternative Plans: Generate alternative approaches to achieve goals
  • User Clarification: Request clarification of ambiguous commands

Evaluation Metrics​

Planning Quality Metrics​

  • Success Rate: Percentage of plans that achieve the goal
  • Plan Optimality: Quality of generated plans compared to optimal
  • Execution Time: Time to generate and execute plans
  • Resource Usage: Computational and energy resources used

User Experience Metrics​

  • Naturalness: How natural the interaction feels to users
  • Efficiency: How efficiently users can communicate with the robot
  • Reliability: Consistency of robot behavior
  • Satisfaction: User satisfaction with the system

Troubleshooting Common Issues​

Planning Problems​

  • Combinatorial Explosion: Too many possible plans to evaluate
  • Incomplete Domain Knowledge: Missing information about the environment
  • Dynamic Environments: Plans invalidated by environmental changes
  • Multi-robot Coordination: Coordination challenges in multi-robot systems

LLM Integration Issues​

  • Hallucination: LLMs generating incorrect or impossible plans
  • Context Window Limitations: Limited context for complex planning
  • Response Inconsistency: Inconsistent responses to similar commands
  • Latency Issues: Slow response times for real-time planning

Exercises​

Exercise 1: LLM Integration

Integrate an LLM for planning:

  • Set up an LLM interface for robotic planning
  • Create a simple command-to-action translation system
  • Test with basic navigation and manipulation commands
  • Evaluate the quality and feasibility of generated plans
Exercise 2: Context-Aware Planning

Implement context-aware planning:

  • Create a system that maintains environmental context
  • Implement planning that considers current robot state
  • Test with commands that require environmental awareness
  • Evaluate how context affects planning decisions
Exercise 3: Plan Execution Integration

Connect planning to action execution:

  • Implement a system that executes generated plans
  • Add monitoring and feedback mechanisms
  • Test complete planning and execution cycles
  • Handle plan failures and recovery

Summary​

Cognitive planning represents the intelligence layer that translates natural language commands into executable robotic actions. By leveraging Large Language Models and integrating them with ROS 2, robots can understand complex user intentions and generate appropriate action sequences. Successful implementation requires careful attention to context management, safety considerations, and integration with existing robotic systems. The combination of symbolic and learning-based planning approaches enables robust and flexible robotic systems capable of natural interaction with humans.