視覚触覚センサを用いた柔軟接触シミュレーションと変形物体操作学習
Soft Contact Simulation and Manipulation Learning of Deformable Objects with Vision-based Tactile Sensor
視覚触覚センサをエンドエフェクタに用い、弾性・塑性・弾塑性変形を再現するシミュレータで強化学習と模倣学習を行い、実機へ転移して変形物体操作を90%の成功率で実現した。
著者: Jianhua Shan, Yuhao Sun, Shixin Zhang, Fuchun Sun, Zixi Chen, Zirong Shen, Cesare Stefanini, Yiyong Yang, Shan Luo, Bin Fang
分類: cs.RO
原文アブストラクト
Deformable object manipulation is a classical and challenging research area in robotics. Compared with rigid object manipulation, this problem is more complex due to the deformation properties including elastic, plastic, and elastoplastic deformation. In this paper, we describe a new deformable object manipulation method including soft contact simulation, manipulation learning, and sim-to-real transfer. We propose a novel approach utilizing Vision-Based Tactile Sensors (VBTSs) as the end-effector in simulation to produce observations like relative position, squeezed area, and object contour, which are transferable to real robots. For a more realistic contact simulation, a new simulation environment including elastic, plastic, and elastoplastic deformations is created. We utilize RL strategies to train agents in the simulation, and expert demonstrations are applied for challenging tasks. Finally, we build a real experimental platform to complete the sim-to-real transfer and achieve a 90% success rate on difficult tasks such as cylinder and sphere. To test the robustness of our method, we use plasticine of different hardness and sizes to repeat the tasks including cylinder and sphere. The experimental results show superior performances of deformable object manipulation with the proposed method.
