# A Bayesian Framework for Active Tactile Object Recognition, Pose Estimation and Shape Transfer Learning

arXiv: https://arxiv.org/abs/2409.06912 · HTML: https://mlsanity.com/paper/2409.06912/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 37 references that are in the map.
- First posted on arXiv 10 Sep 2024.

## Builds on (most cited first, 6 of 6)

- [3D Shape Reconstruction from Vision and Touch](https://mlsanity.com/paper/2007.03778/) (2020)
- [Active 3D Shape Reconstruction from Vision and Touch](https://mlsanity.com/paper/2107.09584/) (2021)
- [3D Shape Perception from Monocular Vision, Touch, and Shape Priors](https://mlsanity.com/paper/1808.03247/) (2018)
- [TouchSDF: A DeepSDF Approach for 3D Shape Reconstruction using Vision-Based Tactile Sensing](https://mlsanity.com/paper/2311.12602/) (2023)
- [Memory Unscented Particle Filter for 6-DOF Tactile Localization](https://mlsanity.com/paper/1607.02757/) (2016)
- [Efficient Visuo-Haptic Object Shape Completion for Robot Manipulation](https://mlsanity.com/paper/2303.04700/) (2023)

## Built on by (most cited first, 1 of 1)

- [Physically Grounded 3D Generative Reconstruction under Hand Occlusion using Proprioception and Multi-Contact Touch](https://mlsanity.com/paper/2604.09100/) (2026)

## Abstract (arXiv)

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 simulation, the proposed framework demonstrated its effectiveness and efficiency in estimating object class and pose for known objects and learning novel shapes. Furthermore, it can recognize previously learned shapes reliably.