ダイナミクス類似度に基づく経験選択で効率的なマルチソース転移学習
Experience Selection Using Dynamics Similarity for Efficient Multi-Source Transfer Learning Between Robots
ロボット間のシステム類似度をν-gapで推定し、転移に有効な経験を選別することで、クアッドロータの逆ダイナミクスモデル学習を効率化した研究。
著者: Michael J. Sorocky, Siqi Zhou, Angela P. Schoellig
分類: cs.RO, cs.LG, cs.SY, eess.SY
原文アブストラクト
In the robotics literature, different knowledge transfer approaches have been proposed to leverage the experience from a source task or robot -- real or virtual -- to accelerate the learning process on a new task or robot. A commonly made but infrequently examined assumption is that incorporating experience from a source task or robot will be beneficial. In practice, inappropriate knowledge transfer can result in negative transfer or unsafe behaviour. In this work, inspired by a system gap metric from robust control theory, the $\nu$-gap, we present a data-efficient algorithm for estimating the similarity between pairs of robot systems. In a multi-source inter-robot transfer learning setup, we show that this similarity metric allows us to predict relative transfer performance and thus informatively select experiences from a source robot before knowledge transfer. We demonstrate our approach with quadrotor experiments, where we transfer an inverse dynamics model from a real or virtual source quadrotor to enhance the tracking performance of a target quadrotor on arbitrary hand-drawn trajectories. We show that selecting experiences based on the proposed similarity metric effectively facilitates the learning of the target quadrotor, improving performance by 62% compared to a poorly selected experience.
