从局部修正到通用技能：利用MEMO改进神经符号策略
From Local Corrections to Generalized Skills: Improving Neuro-Symbolic Policies with MEMO
AI总结：
针对机器人神经符号策略中技能不足的问题，提出MEMO框架，通过收集、聚类和改写多用户自然语言修正，构建检索增强的技能手册，动态扩展技能库，实现新任务泛化。
摘要：
最近的工作使用神经符号框架进行通用操作策略。该框架的优势在于——通过应用现成的视觉和语言模型——机器人可以将复杂任务分解为语义子任务。然而，根本瓶颈在于机器人需要技能将这些子任务具体化为具身运动。技能可以采取多种形式（例如，轨迹片段、运动原语、编码函数），但无论其形式如何，技能都起到约束作用。高层策略只能通过可用技能来接地其语言推理；如果机器人无法为当前任务生成正确的技能，其策略将失败。我们提出通过利用用户反馈来解决这一限制——并动态扩展机器人的技能。当机器人失败时，人类可以直观地解释出了什么问题（例如，“不，再高一点”）。虽然一种简单的方法是在机器人下次遇到类似情况时回忆这段确切的文本，但我们假设通过收集、聚类和改写来自多个用户和任务的自然语言修正，我们可以合成更通用的文本指导和编码技能模板。应用这一假设，我们开发了记忆增强操作（MEMO）。MEMO构建并维护一个从人类反馈和任务成功中收集的检索增强技能手册。在运行时，MEMO从该手册中检索相关文本和代码，使机器人的策略能够在推理多任务人类反馈的同时生成新技能。我们的实验表明，使用MEMO将局部反馈聚合为通用技能模板，能够泛化到现有基线无法胜任的新任务。补充材料见：this https URL
英文摘要：
Recent works use a neuro-symbolic framework for general manipulation policies. The advantage of this framework is that -- by applying off-the-shelf vision and language models -- the robot can break complex tasks down into semantic subtasks. However, the fundamental bottleneck is that the robot needs skills to ground these subtasks into embodied motions. Skills can take many forms (e.g., trajectory snippets, motion primitives, coded functions), but regardless of their form skills act as a constraint. The high-level policy can only ground its language reasoning through the available skills; if the robot cannot generate the right skill for the current task, its policy will fail. We propose to address this limitation -- and dynamically expand the robot's skills -- by leveraging user feedback. When a robot fails, humans can intuitively explain what went wrong (e.g., ``no, go higher''). While a simple approach is to recall this exact text the next time the robot faces a similar situation, we hypothesize that by collecting, clustering, and re-phrasing natural language corrections across multiple users and tasks, we can synthesize more general text guidance and coded skill templates. Applying this hypothesis we develop Memory Enhanced Manipulation (MEMO). MEMO builds and maintains a retrieval-augmented skillbook gathered from human feedback and task successes. At run time, MEMO retrieves relevant text and code from this skillbook, enabling the robot's policy to generate new skills while reasoning over multi-task human feedback. Our experiments demonstrate that using MEMO to aggregate local feedback into general skill templates enables generalization to novel tasks where existing baselines fall short. See supplemental material here: https://collab.me.vt.edu/memo
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
