逆動力学学習のための新たなデータ源
A New Data Source for Inverse Dynamics Learning
フィードバック制御信号から目標加速度で測定される追加の学習信号を導出し、従来のデータ源と組み合わせて逆動力学モデルを効率的に学習する手法を提案・実機検証した。
著者: Daniel Kappler, Franziska Meier, Nathan Ratliff, Stefan Schaal
分類: cs.RO
原文アブストラクト
Modern robotics is gravitating toward increasingly collaborative human robot interaction. Tools such as acceleration policies can naturally support the realization of reactive, adaptive, and compliant robots. These tools require us to model the system dynamics accurately -- a difficult task. The fundamental problem remains that simulation and reality diverge--we do not know how to accurately change a robot's state. Thus, recent research on improving inverse dynamics models has been focused on making use of machine learning techniques. Traditional learning techniques train on the actual realized accelerations, instead of the policy's desired accelerations, which is an indirect data source. Here we show how an additional training signal -- measured at the desired accelerations -- can be derived from a feedback control signal. This effectively creates a second data source for learning inverse dynamics models. Furthermore, we show how both the traditional and this new data source, can be used to train task-specific models of the inverse dynamics, when used independently or combined. We analyze the use of both data sources in simulation and demonstrate its effectiveness on a real-world robotic platform. We show that our system incrementally improves the learned inverse dynamics model, and when using both data sources combined converges more consistently and faster.
