ロボティクスにおける転移学習：次なるブレークスルーか？ その可能性と課題のレビュー
Transfer Learning in Robotics: An Upcoming Breakthrough? A Review of Promises and Challenges
ロボット・タスク・環境の観点から転移学習を統一的に整理し、異なる抽象度での転移や転移ギャップの定量化、負の転移の危険性といった課題をレビューしたポジションペーパー。
著者: Noémie Jaquier, Michael C. Welle, Andrej Gams, Kunpeng Yao, Bernardo Fichera, Aude Billard, Aleš Ude, Tamim Asfour, Danica Kragic
分類: cs.RO, cs.LG
原文アブストラクト
Transfer learning is a conceptually-enticing paradigm in pursuit of truly intelligent embodied agents. The core concept -- reusing prior knowledge to learn in and from novel situations -- is successfully leveraged by humans to handle novel situations. In recent years, transfer learning has received renewed interest from the community from different perspectives, including imitation learning, domain adaptation, and transfer of experience from simulation to the real world, among others. In this paper, we unify the concept of transfer learning in robotics and provide the first taxonomy of its kind considering the key concepts of robot, task, and environment. Through a review of the promises and challenges in the field, we identify the need of transferring at different abstraction levels, the need of quantifying the transfer gap and the quality of transfer, as well as the dangers of negative transfer. Via this position paper, we hope to channel the effort of the community towards the most significant roadblocks to realize the full potential of transfer learning in robotics.
