现实世界的人形机动与强化学习
Ilija Radosavovic1, Tete Xiao1, Bike Zhang1
1University of California, Berkeley CA, USA.
Science robotics
|April 17, 2024
概括
本研究介绍了一种基于学习的控制器,用于人形机器人,使其能够在各种环境中自主移动. 基于变压器的模型适应上下文,在没有再培训的情况下实现强大的现实世界性能.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 类人机器人的经典控制器在一般化和适应新环境方面遇到了困难.
- 自主的人形机器人对于制造业,老年护理和太空探索等应用至关重要.
研究的目的:
- 开发一种完全基于学习的方法,用于现实世界的人形运动.
- 创建一个可以适应各种环境和干扰而不需要重量更新的控制器.
主要方法:
- 开发了一个因果变压器模型,以根据历史观察和行动来预测机器人的行为.
- 该模型在模拟的随机环境中使用大规模的无模型强化学习进行训练.
- 控制器被部署在现实世界的场景,零射击适应.
主要成果:
- 基于学习的控制器成功使人形机器人能够在各种户外地形上行走.
- 控制器表现出对外部干扰的稳定性.
- 该模型表现出特定环境的适应性,而不需要再培训.
结论:
- 使用因果转换器的完全基于学习的方法可以在现实世界中实现强大的,适应性的人形运动.
- 这种方法克服了经典控制器在概括和适应方面的局限性.
- 开发的控制器显示出在复杂,非结构化的环境中自主操作的巨大潜力.
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