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Fast-Reactive Probabilistic Motion Planning for High-Dimensional Robots

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Abstract

Many real-world robotic operations that involve high-dimensional humanoid robots require fast reaction to plan disturbances and probabilistic guarantees over collision risks, whereas most probabilistic motion planning approaches developed for car-like robots cannot be directly applied to high-dimensional robots. In this paper, we present probabilistic Chekov (p-Chekov), a fast-reactive motion planning system that can provide safety guarantees for high-dimensional robots suffering from process noises and observation noises. Leveraging recent advances in machine learning as well as our previous work in deterministic motion planning that integrated trajectory optimization into a sparse roadmap framework, p-Chekov demonstrates its superiority in terms of collision avoidance ability and planning speed in high-dimensional robotic motion planning tasks in complex environments without the convexification of obstacles. Comprehensive theoretical and empirical analysis provided in this paper shows that p-Chekov can effectively satisfy user-specified chance constraints over collision risk in practical robotic manipulation tasks.

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The authors are funded by Massachusetts Institute of Technology while conducting this research.

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  1. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32 Vassar Street, Cambridge, MA, 02139, USA

    Siyu Dai, Andreas Hofmann & Brian Williams

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  1. Siyu Dai
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Dai, S., Hofmann, A. & Williams, B. Fast-Reactive Probabilistic Motion Planning for High-Dimensional Robots. SN COMPUT. SCI. 2, 484 (2021). https://doi.org/10.1007/s42979-021-00878-0

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