EmbodiedRSI提升机器人试错效率

哥伦比亚大学与普林斯顿大学的EmbodiedRSI工作，打造了一套自进化智能体外层框架，让机器人自主判断下一步该探索什么，把实体交互的试错结果，直接转化为可复用的代码与操作技能。 让 Embodied Agent Harness 自主决定“接下来试什么、往哪个方更向新”—— EmbodiedRSI ，通过维护代码与能技的改进假设，优先选择信价息值高、成本低的实验，再据根实际执行果结推动代码技与能共同更新； 同时通过 reward 学习哪些经验得值保留和复用，让每次交都互能更有效指地导后续改进。

在冻结层底机器人型模的情况下，RoboCasa365 未见合组任务成功率最从佳基线的 40.1% 提升至 71.3%，零样本迁移到真机，成率功从 46.0% 提升至 71.3%。 相同 50 次交互算预下，相随比机探索，成功率从 27.1% 提升至 72.9%。

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 EmbodiedEvo, a self-evolving agentic harness that autonomously decides where to explore next and turns the resulting physical interaction into improved code and skills.

EmbodiedEvo realizes this through a Fast-Slow Dual-System Architecture, in which competing code and skill hypotheses are maintained in a Hypothesis Graph.

Value-of-Information Experiment Selection chooses physical experiments that can distinguish these hypotheses. Their outcomes guide Code-Skill Co-Evolution.

The Slow System builds Hierarchical Memory, and Reward-Grounded Memory Learning selects effective memory according to their value for later Fast-System improvement.

On RoboCasa365, EmbodiedEvo reaches 77.0% overall success and 71.3% on Composite-Unseen, compared with 40.1% for the best baseline. EmbodiedEvo also reaches 86.8% overall success on LIBERO-Pro.

Beyond benchmark performance, EmbodiedEvo transfers zero-shot to real-world robot, achieving 71.3% overall success across multiple challenging tasks.