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Train-Once Plan-Anywhere Kinodynamic Motion Planning via Diffusion Trees

Created by
  • Haebom

Author

Yaniv Hassidof, Tom Jurgenson, Kiril Solovey

Outline

This paper addresses the problem of kinodynamic motion planning, which involves calculating collision-free trajectories while respecting a robot's dynamic constraints. Existing sampling-based planners (SBPs) suffer from slow exploration speeds due to random action sampling, while learning-based planners suffer from poor generalization performance and difficulty in ensuring safety. In this paper, we present the "Diffusion Tree (DiTree)" framework, which efficiently guides state-space exploration of SBPs by utilizing diffusion policies (DPs). DiTree combines a DP action sampler trained in a single environment with an RRT planner, providing a safe and efficient solution for complex dynamic systems. Experimental results show that DiTree is three times faster than existing SBPs and improves the success rate by approximately 30%.

Takeaways, Limitations

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Takeaways:
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We have significantly improved the efficiency of sampling-based planners by leveraging diffusion policies.
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It demonstrates generalization performance that allows models trained in a single environment to be applied to diverse environments.
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It overcomes the limitations of existing SBP and learning-based methods, enabling safe and efficient dynamic exercise planning.
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Experimental results demonstrated superior performance in speed and success rate compared to existing SBPs.
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Limitations:
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The performance of DiTree presented in this paper may depend on the specific environment and training data. Additional experiments are needed on various environments and robotic systems.
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The lack of a detailed description of the training process for the diffusion policy necessitates a review of reproducibility.
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Performance in extremely complex or constrained environments requires further research.
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