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Media type:
E-Article
Title:
SACBP: Belief space planning for continuous-time dynamical systems via stochastic sequential action control
Contributor:
Nishimura, Haruki;
Schwager, Mac
Published:
SAGE Publications, 2021
Published in:
The International Journal of Robotics Research, 40 (2021) 10-11, Seite 1167-1195
Language:
English
DOI:
10.1177/02783649211037697
ISSN:
0278-3649;
1741-3176
Origination:
Footnote:
Description:
We propose a novel belief space planning technique for continuous dynamics by viewing the belief system as a hybrid dynamical system with time-driven switching. Our approach is based on the perturbation theory of differential equations and extends sequential action control to stochastic dynamics. The resulting algorithm, which we name SACBP, does not require discretization of spaces or time and synthesizes control signals in near real-time. SACBP is an anytime algorithm that can handle general parametric Bayesian filters under certain assumptions. We demonstrate the effectiveness of our approach in an active sensing scenario and a model-based Bayesian reinforcement learning problem. In these challenging problems, we show that the algorithm significantly outperforms other existing solution techniques including approximate dynamic programming and local trajectory optimization.