Bayesian Tactile Exploration for Compliant Docking with Uncertain Shapes

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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)
Title of host publicationRobotics
Subtitle of host publicationScience and Systems XIV
EditorsHadas Kress-Gazit, Siddhartha S. Srinivasa, Tom Howard, Nikolay Atanasov
PublisherMIT Press Journals
ISBN (Print)9780992374747
StatePublished - 2018
Externally publishedYes
Event14th Robotics: Science and Systems, RSS 2018 - Pittsburgh, United States
Duration: Jun 26 2018Jun 30 2018

Publication series

NameRobotics: Science and Systems
ISSN (Electronic)2330-765X


Conference14th Robotics: Science and Systems, RSS 2018
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Artificial Intelligence
  • Control and Systems Engineering
  • Electrical and Electronic Engineering


Dive into the research topics of 'Bayesian Tactile Exploration for Compliant Docking with Uncertain Shapes'. Together they form a unique fingerprint.

Cite this