Bayesian Tactile Exploration for Compliant Docking with Uncertain Shapes

Research output: Contribution to journalArticlepeer-review


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 robot's 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.

Original languageEnglish (US)
Article number8755306
Pages (from-to)1084-1096
Number of pages13
JournalIEEE Transactions on Robotics
Issue number5
StatePublished - Oct 2019


  • Climbing robots
  • force and tactile sensing
  • motion and path planning
  • probability and statistical methods

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering


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