Transformerベースの触覚制御方策による安定把持の学習
Learning Stable Robot Grasping with Transformer-based Tactile Control Policies
触覚フィードバックから回転ずれや滑りを検出し、重心が未知で移動する物体に対して把持位置と把持力を同時に最適化するTransformer強化学習フレームワークを提案し、シミュレーションと実機でゼロショット転移を実現した。
著者: En Yen Puang, Zechen Li, Chee Meng Chew, Shan Luo, Yan Wu
分類: cs.RO
原文アブストラクト
Measuring grasp stability is an important skill for dexterous robot manipulation tasks, which can be inferred from haptic information with a tactile sensor. Control policies have to detect rotational displacement and slippage from tactile feedback, and determine a re-grasp strategy in term of location and force. Classic stable grasp task only trains control policies to solve for re-grasp location with objects of fixed center of gravity. In this work, we propose a revamped version of stable grasp task that optimises both re-grasp location and gripping force for objects with unknown and moving center of gravity. We tackle this task with a model-free, end-to-end Transformer-based reinforcement learning framework. We show that our approach is able to solve both objectives after training in both simulation and in a real-world setup with zero-shot transfer. We also provide performance analysis of different models to understand the dynamics of optimizing two opposing objectives.
