AI ENHANCED ANALYSIS
LLM-Assisted Plan Execution for Robots in Dynamic Environments
This paper introduces a novel framework for robotics plan execution, integrating Large Language Models (LLMs) to enhance adaptability in dynamic environments. It uses LLMs for proactive environmental forecasting and informed plan evaluation, moving beyond reactive replanning to improve robot efficiency and robustness.
The Problem: Traditional robotic planning systems struggle with highly dynamic and unpredictable environments, often relying on rigid, reactive replanning that cancels all ongoing actions and regenerates plans from scratch. This leads to inefficiencies, disruptions, and a lack of adaptability when environmental conditions or goals change frequently.
Executive Impact
Our analysis highlights key performance improvements and strategic advantages for enterprise robotics.
24s
Reduction in Goal Completion Time (Simulated Dynamic World)
21s
Reduction in Goal Completion Time (Real Dynamic World)
90%
Reduction in Worst-Case Completion Variance
3.7s
Average LLM Inference Latency
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Zero-shot
LLM Usage
The LLM acts exclusively as an evaluator, not a plan generator, selecting from formally valid plans. This mitigates hallucination risks and leverages the LLM's semantic evaluation capabilities without requiring specific training.
Enterprise Process Flow
Current PDDL Problem State (Ft, Gt, πt)
→
Environmental Forecasting (LLM)
→
Predicted Future State (yt)
→
Candidate Plans (from PDDL planners)
→
Plan Evaluator (LLM)
→
Optimal Plan (π*)
| Feature | Classical Replanning | LLM-Assisted Plan Repair |
|---|---|---|
| Decision-making |
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| Execution Continuity |
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| Efficiency in Dynamic Worlds |
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24s
Time Reduction (Simulated)
The LLM approach achieved a 24-second reduction in goal achievement time in simulated dynamic environments compared to the baseline, enabling the robot to visit four additional waypoints.
TIAGO Robot in Dynamic Indoor Navigation
The framework was validated on a TIAGO robot in real-world and simulated indoor navigation scenarios. The robot had to pick up and deliver objects between waypoints, facing dynamic disconnections and changing goals.
Challenge: Maintaining plan validity and efficiency when pathways are intermittently blocked (e.g., wp2-wp5 connection removed) or new goals are introduced, without causing unnecessary replanning or disruptions.
Solution: The LLM-assisted system proactively forecasts disconnections and evaluates alternative plans considering future states. It then selects optimal plans that either bypass blocked routes or leverage existing progress, ensuring minimal disruption.
Results: The robot successfully navigated dynamic environments with significantly reduced task completion times and improved predictability compared to classical methods. For example, when a connection was blocked, the LLM suggested alternative routes, preventing plan failures and allowing continuous operation.
PlanSys2
Foundation Framework
The system is implemented within PlanSys2, a PDDL-based symbolic planning framework for ROS 2, extending its capabilities for continuous planning, plan repair, and dynamic adaptation.
Calculate Your Potential ROI
Estimate the efficiency gains and cost savings your enterprise could achieve with LLM-assisted robotics.
Annual Savings
$0
Hours Reclaimed Annually
0
Your Implementation Roadmap
A strategic overview of how LLM-assisted robotics can be integrated into your enterprise, from foundation to advanced deployment.
Phase 1: Foundation & LLM Integration
Establish the core PlanSys2 framework and integrate the Mistral API. Develop initial Environmental Forecasting and Plan Evaluator modules. Validate basic plan repair mechanisms in simulation.
Phase 2: Dynamic Environment Adaptation
Refine LLM prompting for proactive forecasting and semantic plan evaluation in complex dynamic scenarios. Conduct extensive testing in simulated environments with varying connectivity changes and goal dynamics.
Phase 3: Real-World Deployment & Validation
Deploy the LLM-assisted framework on a physical TIAGO robot. Validate performance in real-world indoor navigation tasks, measuring goal completion times and robustness under physical uncertainties. Collect and analyze execution logs.
Phase 4: Optimization & Future Expansion
Optimize LLM query frequency and latency handling. Explore extensions to multi-agent systems, unstructured dynamic manipulation, and integration with RAG for long-term experience-based learning and overcoming context window limitations.
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