通过循环强化学习精确和灵巧的机器人操纵
Jianlan Luo1, Charles Xu1, Jeffrey Wu1
1Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720, USA.
Science robotics
|August 20, 2025
概括
这项研究引入了用于机器人操纵的人在循环中强化学习 (RL) 系统. 基于视觉的方法使机器人能够在现实世界中快速有效地学习复杂的任务.
科学领域:
- 机器人技术
- 人工智能
- 机器学习
背景情况:
- 机器人操作面临着传统方法的挑战,需要大量的手动设计和数据收集.
- 可靠性,速度和强度的局限性阻碍了现有机器人操纵技术的实际部署.
- 强化学习 (RL) 提供自主技能获取,但在现实应用中难以获得效率和安全性.
研究的目的:
- 开发一个基于视觉的强化学习 (RL) 系统,用于灵巧的机器人操纵.
- 通过提高样本效率和安全性实现自主技能获取,克服现有方法的局限性.
- 在现实的工业条件下证明系统在各种复杂的操作任务中的能力.
主要方法:
- 人类演示和纠正与样本效率RL算法的整合.
- 使用基于视觉的系统在现实世界中直接学习RL政策.
- 系统级设计,包括精确组装,动态操纵和双臂协调任务.
主要成果:
- 在各种灵巧的操作任务中取得了强大的表现,包括精确的组装和双臂协调.
- 在任务成功率上超过基线方法2倍,平均执行任务速度快1.8倍.
- 在1到2.5小时的实践训练中证明了近乎完美的成功率.
结论:
- 强化学习可以在实践训练时间内直接学习复杂的基于视觉的机器人操纵策略.
- 人在循环的方法显著提高了真实世界机器人操纵的样品效率和性能.
- 这项工作为工业应用和研究中先进的机器人操纵技术铺平了道路.
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