Resisting Stochastic Risks in Diffusion Planners with the Trajectory Aggregation Tree

Lang Feng, Pengjie Gu, Bo An, Gang Pan·May 28, 2024

Summary

The paper introduces the Trajectory Aggregation Tree (TAT), a novel approach for enhancing diffusion planners in stochastic environments. TAT addresses the issue of infeasible trajectories by combining historical and current trajectories into a dynamic tree structure, prioritizing reliable states and reducing reliance on unreliable samples. This method, which can be applied to existing models without additional training, improves planner performance, stability, and efficiency, particularly in long-horizon and sparse-reward tasks. TAT outperforms baseline planners, showing consistent improvements in performance and up to three times faster planning, while mitigating stochastic risks. The study evaluates TAT on various planners and tasks, demonstrating its effectiveness in handling artifacts and adapting to changing environments.

Key findings

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Tables

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Introduction
Background
Evolution of diffusion planners in stochastic environments
Challenges with infeasible trajectories and reliance on unreliable samples
Objective
Introduce TAT: a novel method for improving planner performance
Enhance stability and efficiency in long-horizon, sparse-reward tasks
Aim to mitigate stochastic risks without additional model training
Method
Data Collection
Historical and Current Trajectory Integration
Combining past and present trajectory data
Sampling Strategy
Selection of reliable states for tree construction
Data Preprocessing
Tree Structure Construction
Dynamic aggregation of trajectories into a tree
Hierarchical organization of states
State Reliability Assessment
Prioritization based on confidence in state validity
TAT Integration with Existing Planners
Plug-and-play compatibility with various planners
Adaptation to different task scenarios
Performance Evaluation
Baseline Comparison
Comparative analysis with traditional planners
Metrics: performance, stability, and planning time
Task Variations
Evaluation on diverse environments and tasks
Handling artifacts and adapting to changing conditions
Results and Analysis
Quantitative improvements in performance
Speedup in planning efficiency (up to three times faster)
Conclusion
Summary of TAT's advantages
Implications for future research in stochastic planning
Potential real-world applications
Future Work
Extensions and improvements to TAT
Exploration of TAT in real-world scenarios
Integration with emerging planning techniques
Basic info
papers
machine learning
artificial intelligence
Advanced features