宇宙ロボティクスへの強化学習の応用：高速学習と実環境への信頼性の高い転移
Towards Reinforcement Learning for Space Robotics: Fast Training and Reliable Real-World Transfer
単一の民生用GPUで20分未満の学習により四足歩行ロボットが多様な実地形（月面模擬施設含む）を走破できる強化学習フレームワークを実証した。
著者: Abhishek Naik, Michael Wu, Michael O'Sullivan, Colin Bellinger, Yunli Wang
分類: cs.RO, cs.AI
原文アブストラクト
This work demonstrates the use of a fast simulation-based training framework to learn control policies using reinforcement learning. The hardware requirements are minimal and the simulation-learned policies transfer reliably to the real world. With this setup, we show that a quadruped robot can learn to traverse diverse real-world terrains reliably---including in a lunar-analog facility---after less than 20 minutes of training on a single consumer-grade GPU. These results provide a glimpse of a future where reinforcement learning is a crucial tool in developing more capable and robust space robotic systems. Video demonstrations are available at https://youtu.be/vUFJiZdJaQ0.