触覚による位置推定のためのベイズフィルタモデルの学習
Learning Bayes Filter Models for Tactile Localization
触覚フィードバックと環境の視覚マップを条件とする観測モデルを学習し、ベイズフィルタでロボットグリッパの位置を推定する手法を提案。シミュレーションで自己教師あり学習し実機に転移、卓上での物体との接触による位置推定を実現した。
著者: Tarik Kelestemur, Colin Keil, John P. Whitney, Robert Platt, Taskin Padir
分類: cs.RO
原文アブストラクト
Localizing and tracking the pose of robotic grippers are necessary skills for manipulation tasks. However, the manipulators with imprecise kinematic models (e.g. low-cost arms) or manipulators with unknown world coordinates (e.g. poor camera-arm calibration) cannot locate the gripper with respect to the world. In these circumstances, we can leverage tactile feedback between the gripper and the environment. In this paper, we present learnable Bayes filter models that can localize robotic grippers using tactile feedback. We propose a novel observation model that conditions the tactile feedback on visual maps of the environment along with a motion model to recursively estimate the gripper's location. Our models are trained in simulation with self-supervision and transferred to the real world. Our method is evaluated on a tabletop localization task in which the gripper interacts with objects. We report results in simulation and on a real robot, generalizing over different sizes, shapes, and configurations of the objects.
