Bayesian Tactile Exploration for Compliant Docking With Uncertain Shapes
Bayesian Tactile Exploration for Compliant Docking With Uncertain Shapes
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DOI：
10.1109/tro.2019.2921144
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发表时间：
2019-10-01
影响因子：
7.8
通讯作者：
Hauser, Kris
中科院分区：
文献类型：
--
作者：
Hauser, Kris
This paper presents a Bayesian approach for active tactile exploration of a planar shape in the presence of both localization and shape uncertainty. The goal is to dock the robots end-effector against the shape-reaching a point of contact that resists a desired load-with as few probing actions as possible. The proposed method repeatedly performs inference, planning, and execution steps. Given a prior probability distribution over object shape and sensor readings from previously executed motions, the posterior distribution is inferred using a novel and efficient Hamiltonian Monte Carlo method. The optimal docking site is chosen to maximize docking probability, using a closed-form probabilistic simulation that accepts rigid and compliant motion models under Coulomb friction. Numerical experiments demonstrate that this method requires fewer exploration actions to dock than heuristics and information-gain strategies.
