SLAMM触觉:从平板推力中实时推断形状和姿势 (Tactile SLAM: Real-time inference of shape and pose from planar pushing)

from arxiv, Camera-ready version to be presented at the 2021 IEEE International Conference on Robotics and Automation (ICRA 2021). For associated video file, see https://youtu.be/wdyagx5MM40

Tactile perception is central to robot manipulation in unstructured environments. However, it requires contact, and a mature implementation must infer object models while also accounting for the motion induced by the interaction. In this work, we present a method to estimate both object shape and pose in real-time from a stream of tactile measurements. This is applied towards tactile exploration of an unknown object by planar pushing. We consider this as an online SLAM problem with a nonparametric shape representation. Our formulation of tactile inference alternates between Gaussian process implicit surface regression and pose estimation on a factor graph. Through a combination of local Gaussian processes and fixed-lag smoothing, we infer object shape and pose in real-time. We evaluate our system across different objects in both simulated and real-world planar pushing tasks.

翻译：触觉感知是机器人在非结构化环境中操纵的核心。但是, 它需要联系, 成熟的操作必须推断对象模型, 同时还要计算相互作用引发的动作。在这项工作中, 我们提出一种方法来根据触觉测量流来估计物体形状和实时显示物体形状。这是用于通过平板推力对未知物体进行触觉探索的。我们认为这是一个在线的 SLAM 问题, 具有非对称形状表示。我们在高森进程隐含的表面回归和对系数图进行估计之间生成的触觉推断替代。我们通过将本地高山进程和固定层滑动结合起来, 我们推断物体形状和实时显示。我们评估了模拟和现实地平板推力任务中不同物体的系统。