基于演示的自适应课程 applied to sim-to-real：多指节机器人
机器人学
2024-09-16 v2 机器学习
摘要
我们提出了 DemoStart，一种 novel 的自适应课程强化学习方法，能够仅凭稀疏奖励和少量仿真中演示，在配备三指机器人手臂的机械臂上学习复杂的操纵行为。仿真学习大幅度减少了行为生成的开发周期，借助 domain randomization 技术实现零-shot sim-to-real 迁移。迁移到的策略直接从多个摄像机的原始像素和机器人本体感知学习。我们的做法优于从真实机器人上学习的策略，仅需仿真中收集的演示数量减少 100 倍。更多细节和视频请参见 https://sites.google.com/view/demostart。
关键词
引用
@article{arxiv.2409.06613,
title = {DemoStart: Demonstration-led auto-curriculum applied to sim-to-real with multi-fingered robots},
author = {Maria Bauza and Jose Enrique Chen and Valentin Dalibard and Nimrod Gileadi and Roland Hafner and Murilo F. Martins and Joss Moore and Rugile Pevceviciute and Antoine Laurens and Dushyant Rao and Martina Zambelli and Martin Riedmiller and Jon Scholz and Konstantinos Bousmalis and Francesco Nori and Nicolas Heess},
journal= {arXiv preprint arXiv:2409.06613},
year = {2024}
}
备注
15 pages total with 7 pages of appendix. 9 Figures, 4 in the main text and 5 in the appendix