連合模倣学習：異種センサデータを持つクラウドロボットシステムのための新フレームワーク
Federated Imitation Learning: A Novel Framework for Cloud Robotic Systems with Heterogeneous Sensor Data
クラウドロボットシステム向けに、各ロボットの異種知識をクラウドで融合しガイドモデルを生成して配布する連合模倣学習フレームワークFILを提案し、自動運転タスクで模倣学習の効率向上を示した。
著者: Boyi Liu, Lujia Wang, Ming Liu, Cheng-Zhong Xu
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
Humans are capable of learning a new behavior by observing others to perform the skill. Similarly, robots can also implement this by imitation learning. Furthermore, if with external guidance, humans can master the new behavior more efficiently. So, how can robots achieve this? To address the issue, we present a novel framework named FIL. It provides a heterogeneous knowledge fusion mechanism for cloud robotic systems. Then, a knowledge fusion algorithm in FIL is proposed. It enables the cloud to fuse heterogeneous knowledge from local robots and generate guide models for robots with service requests. After that, we introduce a knowledge transfer scheme to facilitate local robots acquiring knowledge from the cloud. With FIL, a robot is capable of utilizing knowledge from other robots to increase its imitation learning in accuracy and efficiency. Compared with transfer learning and meta-learning, FIL is more suitable to be deployed in cloud robotic systems. Finally, we conduct experiments of a self-driving task for robots (cars). The experimental results demonstrate that the shared model generated by FIL increases imitation learning efficiency of local robots in cloud robotic systems.
