TacGNN：視覚なしで触覚によるハンド内操作を学習するブラインドロボット
TacGNN:Learning Tactile-based In-hand Manipulation with a Blind Robot
触覚信号から物体状態を推定するグラフニューラルネットワークTacGNNを提案し、視覚を使わずにロボットハンドがハンド内で物体を操作する強化学習を実現した。
著者: Linhan Yang, Bidan Huang, Qingbiao Li, Ya-Yen Tsai, Wang Wei Lee, Chaoyang Song, Jia Pan
分類: cs.RO
原文アブストラクト
In this paper, we propose a novel framework for tactile-based dexterous manipulation learning with a blind anthropomorphic robotic hand, i.e. without visual sensing. First, object-related states were extracted from the raw tactile signals by a graph-based perception model - TacGNN. The resulting tactile features were then utilized in the policy learning of an in-hand manipulation task in the second stage. This method was examined by a Baoding ball task - simultaneously manipulating two spheres around each other by 180 degrees in hand. We conducted experiments on object states prediction and in-hand manipulation using a reinforcement learning algorithm (PPO). Results show that TacGNN is effective in predicting object-related states during manipulation by decreasing the RMSE of prediction to 0.096cm comparing to other methods, such as MLP, CNN, and GCN. Finally, the robot hand could finish an in-hand manipulation task solely relying on the robotic own perception - tactile sensing and proprioception. In addition, our methods are tested on three tasks with different difficulty levels and transferred to the real robot without further training.
