LEARNING
STEAM：面向真实世界机器人学习的自监督时序集成优势建模
Zhihao Liu, Qiuyi Gu, Yitao Wang, Dongming Qiao, Yixian Zhang, Shuaihang Chen, Liangzhi Shi, Tianxing Zhou, Zefang Huang, Kang Chen, Zhen Guo, Quanlu Zhang, Jincheng Yu, Xiaodan Liang, Guoliang Fan, Yu Wang, Feng Gao, Xinlei Chen, Chao Yu
- 发表年份
- 2026
- 访问权限
- 开放获取
摘要
本文提出了一种名为STEAM的无标签方法，通过自监督时序集成优势建模从专家演示中学习帧级优势，以区分机器人轨迹中的有效进步与停滞、失败和恢复。在多种真实世界操作任务中，STEAM结合CFGRL将策略成功率提升了16.2%至59%。
关键词
self-supervised learningtemporal ensembleadvantage modelingrobot learningreal-world manipulation
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