EmbodiedRSI: Active Continual Robot Learning Through Hypothesis-Guided Co-Evolution· EmbodiedRSI: 假设引导的主动持续机器人学习
Robot foundation models provide strong visuomotor control, yet their performance can degrade when object positions or task instructions change. Further improvements often require post-training on substantial robot data, which can be costly to collect through methods such as teleoperation. Agentic harnesses can adapt around the model, but current self-evolving harnesses use robot trials inefficiently when deciding which code and skill changes to pursue. We introduce EmbodiedRSI, a self-evolving agentic harness that autonomously decides where to explore next and turns the resulting physical interaction into improved code and skills. EmbodiedRSI realizes this through a Fast-Slow Dual-System Architecture, in which competing code and skill hypotheses are maintained in a Hypothesis Graph. Value-
提出了一种通过假设引导的主动持续学习提升机器人性能的方法。
- 核心方法
- EmbodiedRSI 采用快速-慢速双系统架构，通过假设图维护竞争的代码和技能假设，自主决定探索方向并转化为更优的代码和技能。
- 适合谁读
- 研究者、工程师
- 要解决的问题
- 机器人基础模型在物体位置或任务指令变化时性能下降，且现有自我进化框架对数据使用效率低。
- 关键实验
- 未提供
- 主要贡献
- 提高数据使用效率，减少人为干预，实现机器人的自主持续学习和适应。
- 意义与局限
- 有助于提升机器人的适应能力，减少训练成本，但当前缺乏实验验证。