A Bayesian Framework for Active Tactile Object Recognition, Pose Estimation and Shape Transfer Learning
This work addresses the problem of enabling robots to explore and understand objects through touch, which is incremental as it builds on existing Bayesian and tactile methods.
The paper tackles active tactile object recognition, pose estimation, and shape transfer learning for robots by combining a particle filter and Gaussian process implicit surface in a Bayesian framework, demonstrating effectiveness in simulation for known objects and novel shape learning.
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.