[2409.06912] A Bayesian Framework for Active Tactile Object Recognition, Pose Estimation and Shape Transfer Learning

Computer Science > Robotics arXiv:2409.06912 (cs) [Submitted on 10 Sep 2024 (v1), last revised 4 Mar 2026 (this version, v4)] Title:A Bayesian Framework for Active Tactile Object Recognition, Pose Estimation and Shape Transfer Learning Authors:Haodong Zheng, Andrei Jalba, Raymond H. Cuijpers, Wijnand IJsselsteijn, Sanne Schoenmakers View a PDF of the paper titled A Bayesian Framework for Active Tactile Object Recognition, Pose Estimation and Shape Transfer Learning, by Haodong Zheng and 4 other authors View PDF HTML (experimental) Abstract:As humans can explore and understand the world through active touch, similar capability is desired for robots. In this paper, we address the problem of active tactile object recognition, pose estimation and shape transfer learning, where a customized particle filter (PF) and Gaussian process implicit surface (GPIS) is combined in a unified Bayesian framework. Upon new tactile input, the customized PF updates the joint distribution of the object class and object pose while tracking the novelty of the object. Once a novel object is identified, its shape will be reconstructed using GPIS. By grounding the prior of the GPIS with the maximum-a-posteriori (MAP) estimation from the PF, the knowledge about known shapes can be transferred to learn novel shapes. An exploration procedure based on global shape estimation is proposed to guide active data acquisition and terminate the exploration upon sufficient information. Through experiments in simu...