TIAGo RL: 触覚データを用いた移動ロボット向け強化学習シミュレーション環境
TIAGo RL: Simulated Reinforcement Learning Environments with Tactile Data for Mobile Robots
サービスロボットTIAGo向けに、実機の触覚グリッパに似たセンサ計測を生成するオープンソースの強化学習環境を構築し、力制御方策の予備的な学習結果を従来のPI制御と比較した。
著者: Luca Lach, Francesco Ferro, Robert Haschke
分類: cs.RO, cs.AI
原文アブストラクト
Tactile information is important for robust performance in robotic tasks that involve physical interaction, such as object manipulation. However, with more data included in the reasoning and control process, modeling behavior becomes increasingly difficult. Deep Reinforcement Learning (DRL) produced promising results for learning complex behavior in various domains, including tactile-based manipulation in robotics. In this work, we present our open-source reinforcement learning environments for the TIAGo service robot. They produce tactile sensor measurements that resemble those of a real sensorised gripper for TIAGo, encouraging research in transfer learning of DRL policies. Lastly, we show preliminary training results of a learned force control policy and compare it to a classical PI controller.
