ALAN: 実世界で自律的に探索するロボットエージェント
ALAN: Autonomously Exploring Robotic Agents in the Real World
環境変化を指標に不確実性を最大化することで、実世界のキッチン環境でロボットが自律的に探索し、目標画像で指定された操作スキルを効率的に学習する手法を提案。
著者: Russell Mendonca, Shikhar Bahl, Deepak Pathak
分類: cs.RO, cs.AI, cs.CV, cs.LG, cs.SY, eess.SY
原文アブストラクト
Robotic agents that operate autonomously in the real world need to continuously explore their environment and learn from the data collected, with minimal human supervision. While it is possible to build agents that can learn in such a manner without supervision, current methods struggle to scale to the real world. Thus, we propose ALAN, an autonomously exploring robotic agent, that can perform tasks in the real world with little training and interaction time. This is enabled by measuring environment change, which reflects object movement and ignores changes in the robot position. We use this metric directly as an environment-centric signal, and also maximize the uncertainty of predicted environment change, which provides agent-centric exploration signal. We evaluate our approach on two different real-world play kitchen settings, enabling a robot to efficiently explore and discover manipulation skills, and perform tasks specified via goal images. Website at https://robo-explorer.github.io/
