見るより感じろ：動作適応とインピーダンス制御による関節物体操作のsim-to-real RL
Watch Less, Feel More: Sim-to-Real RL for Generalizable Articulated Object Manipulation via Motion Adaptation and Impedance Control
視覚入力への依存を減らし、観測履歴から物体の運動と特性を推定、インピーダンス制御を組み合わせることで、未見の関節物体を実世界で84%の成功率で操作できる強化学習パイプラインを提案。
著者: Tan-Dzung Do, Nandiraju Gireesh, Jilong Wang, He Wang
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
Articulated object manipulation poses a unique challenge compared to rigid object manipulation as the object itself represents a dynamic environment. In this work, we present a novel RL-based pipeline equipped with variable impedance control and motion adaptation leveraging observation history for generalizable articulated object manipulation, focusing on smooth and dexterous motion during zero-shot sim-to-real transfer. To mitigate the sim-to-real gap, our pipeline diminishes reliance on vision by not leveraging the vision data feature (RGBD/pointcloud) directly as policy input but rather extracting useful low-dimensional data first via off-the-shelf modules. Additionally, we experience less sim-to-real gap by inferring object motion and its intrinsic properties via observation history as well as utilizing impedance control both in the simulation and in the real world. Furthermore, we develop a well-designed training setting with great randomization and a specialized reward system (task-aware and motion-aware) that enables multi-staged, end-to-end manipulation without heuristic motion planning. To the best of our knowledge, our policy is the first to report 84\% success rate in the real world via extensive experiments with various unseen objects.
