Today's robot policies exhibit subpar performance when faced with the challenge of generalizing to novel environments. Human corrective feedback is a crucial form of guidance to enable such generalization. However, adapting to and learning from online human corrections is a non-trivial endeavor: not only do robots need to remember human feedback over time to retrieve the right information in new settings and reduce the intervention rate, but also they would need to be able to respond to feedback that can be arbitrary corrections about high-level human preferences to low-level adjustments to skill parameters. In this work, we present Distillation and Retrieval of Online Corrections (DROC), a large language model (LLM)-based system that can respond to arbitrary forms of language feedback, distill generalizable knowledge from corrections, and retrieve relevant past experiences based on textual and visual similarity for improving performance in novel settings. DROC is able to respond to a sequence of online language corrections that address failures in both high-level task plans and low-level skill primitives. We demonstrate that DROC effectively distills the relevant information from the sequence of online corrections in a knowledge base and retrieves that knowledge in settings with new task or object instances. DROC outperforms other techniques that directly generate robot code via LLMs by using only half of the total number of corrections needed in the first round and requires little to no corrections after two iterations. We show further results, videos, prompts and code on https://sites.google.com/stanford.edu/droc .

通过Distillation and Retrieval of Online Corrections (DROC)这个基于大型语言模型（LLM）的系统，我们展示了一种能够回应任意形式的语言反馈、从纠正中提取通用知识以及基于文本和视觉相似性检索相关的先前经验来提高在新环境中表现的方法。DROC能够回应一系列在线语言纠正，解决高级任务计划和低级技能元素的失败，并证明在新的任务或对象实例中，DROC能够有效地提取在线纠正序列中的相关信息并检索该知识。与直接生成机器人代码的LLM相比，DROC只需要一半的纠正次数，并且在两轮迭代后几乎不需要纠正，从而提升了性能。

通过语言修正提炼和检索机器人操作的泛化知识