ドメイン好奇心：ドメイン適応のための効率的なデータ収集戦略の学習
Domain Curiosity: Learning Efficient Data Collection Strategies for Domain Adaptation
環境の未知部分を学習するためのデータを効率的に集める探索方策を、学習そのものを報酬として訓練する手法を提案し、シミュレーションと実機の触覚探索で有効性を示した。
著者: Karol Arndt, Oliver Struckmeier, Ville Kyrki
分類: cs.LG, cs.RO
原文アブストラクト
Domain adaptation is a common problem in robotics, with applications such as transferring policies from simulation to real world and lifelong learning. Performing such adaptation, however, requires informative data about the environment to be available during the adaptation. In this paper, we present domain curiosity -- a method of training exploratory policies that are explicitly optimized to provide data that allows a model to learn about the unknown aspects of the environment. In contrast to most curiosity methods, our approach explicitly rewards learning, which makes it robust to environment noise without sacrificing its ability to learn. We evaluate the proposed method by comparing how much a model can learn about environment dynamics given data collected by the proposed approach, compared to standard curious and random policies. The evaluation is performed using a toy environment, two simulated robot setups, and on a real-world haptic exploration task. The results show that the proposed method allows data-efficient and accurate estimation of dynamics.
