BayesContact: Uncertain Pose Estimation via Visuo-Tactile Proposals and Simulation-based Inference

Abstract:Contact-rich manipulation requires pose estimates that are often more accurate than what depth-only sensing provides. Existing methods, relying on vision and contact, employ costly offline training procedures that need to be retrained for new environments and geometries. We propose BayesContact, a Simulation-Based Inference framework for visuo-tactile pose estimation in peg-in-hole insertion. BayesContact maintains a particle belief over object pose and fuses depth observations with force/torque-derived contact evidence. We employ simulation based forward models to approximate these observation likelihoods. For each pose hypothesis, a renderer predicts depth measurements and a physics simulator predicts contact outcomes under guarded probing actions; both are scored against real observations to update the belief. The resulting multimodal belief also enables information-gain-based probing for active disambiguation. Across simulated geometries and real-robot experiments, BayesContact improves pose observability and insertion success over vision-only inference by 30%

Robotics (cs.RO)

arXiv:2607.16123 [cs.RO]

(or arXiv:2607.16123v2 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2607.16123 Focus to learn more arXiv-issued DOI via DataCite

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