触覚による姿勢推定と未知物体操作のための方策学習
Tactile Pose Estimation and Policy Learning for Unknown Object Manipulation
学習した触覚観測モデルとベイズフィルタで未知物体の姿勢を推定し、その信念を用いた深層強化学習方策をシミュレーションから実機へ転移して物体操作を行う手法を提案した。
著者: Tarik Kelestemur, Robert Platt, Taskin Padir
分類: cs.RO
原文アブストラクト
Object pose estimation methods allow finding locations of objects in unstructured environments. This is a highly desired skill for autonomous robot manipulation as robots need to estimate the precise poses of the objects in order to manipulate them. In this paper, we investigate the problems of tactile pose estimation and manipulation for category-level objects. Our proposed method uses a Bayes filter with a learned tactile observation model and a deterministic motion model. Later, we train policies using deep reinforcement learning where the agents use the belief estimation from the Bayes filter. Our models are trained in simulation and transferred to the real world. We analyze the reliability and the performance of our framework through a series of simulated and real-world experiments and compare our method to the baseline work. Our results show that the learned tactile observation model can localize the pose of novel objects at 2-mm and 1-degree resolution for position and orientation, respectively. Furthermore, we experiment on a bottle opening task where the gripper needs to reach the desired grasp state.
