共有すべきか否か？ロボット間経験転送の性能保証と非対称性
To Share or Not to Share? Performance Guarantees and the Asymmetric Nature of Cross-Robot Experience Transfer
ロボット間で経験を転送する際の追従誤差の理論的限界を導出し、ベイズ最適化でデータから推定する手法を提案。クアッドロータ実験で正の転送を保証できることを示した。
著者: Michael J. Sorocky, Siqi Zhou, Angela P. Schoellig
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
In the robotics literature, experience transfer has been proposed in different learning-based control frameworks to minimize the costs and risks associated with training robots. While various works have shown the feasibility of transferring prior experience from a source robot to improve or accelerate the learning of a target robot, there are usually no guarantees that experience transfer improves the performance of the target robot. In practice, the efficacy of transferring experience is often not known until it is tested on physical robots. This trial-and-error approach can be extremely unsafe and inefficient. Building on our previous work, in this paper we consider an inverse module transfer learning framework, where the inverse module of a source robot system is transferred to a target robot system to improve its tracking performance on arbitrary trajectories. We derive a theoretical bound on the tracking error when a source inverse module is transferred to the target robot and propose a Bayesian-optimization-based algorithm to estimate this bound from data. We further highlight the asymmetric nature of cross-robot experience transfer that has often been neglected in the literature. We demonstrate our approach in quadrotor experiments and show that we can guarantee positive transfer on the target robot for tracking random periodic trajectories.
