用贝叶斯框架让机器人主动触觉识别物体并学习新形状。
A Bayesian Framework for Active Tactile Object Recognition, Pose Estimation and Shape Transfer Learning
- 结合定制粒子滤波与高斯过程隐表面，统一建模物体识别与姿态估计。
- 新物体被识别后，通过GPIS重建形状，且可利用已知形状先验加速学习。
- 基于全局形状估计的探索策略，能智能获取数据并适时终止采集。
人类通过主动触摸探索世界，机器人亦需具备类似能力。本文提出一种统一的贝叶斯框架，用于主动触觉物体识别、姿态估计与形状迁移学习。该框架融合定制粒子滤波（PF）与高斯过程隐表面（GPIS），在接收新触觉输入时，由PF联合更新物体类别与姿态的后验分布，并检测物体新颖性；一旦发现新物体，即通过GPIS重建其形状。通过将GPIS的先验基于PF的后验最大估计（MAP），已知形状的知识可有效迁移到新形状的学习中。此外，提出一种基于全局形状估计的探索策略，指导主动数据采集，并在信息充分时终止探索。仿真实验表明，该框架在已知物体的类别与姿态估计上高效准确，且能可靠识别先前学习过的形状，同时有效学习新形状。
原文摘要 · Abstract (English)
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.
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