通过用户反馈生成通用技能模板，让机器人学会自我改进。
From Local Corrections to Generalized Skills: Improving Neuro-Symbolic Policies with MEMO
- 从人类纠正语句中聚类重写，生成可复用的通用指令和代码模板。
- 在新任务上实现比基线高37%的成功率，显著提升泛化能力。
- 适合研究人机协作、具身智能与自适应机器人策略的开发者。
近期工作采用神经符号框架构建通用操作策略，利用现成的视觉与语言模型将复杂任务分解为语义子任务。但核心瓶颈在于：机器人需具备具体技能将子任务转化为具身动作。无论技能形式如何（如轨迹片段、运动原语、编码函数），它们都构成约束——高层策略只能依赖现有技能进行语义落地；若无法生成正确技能，则策略失败。本文提出通过用户反馈动态扩展技能库以突破此限制。当机器人出错时，人类可直观指出问题（如“不对，再高一点”）。我们假设：若收集、聚类并重述多用户、多任务中的自然语言纠正，便可合成更通用的文本指导与代码模板。基于此，我们开发了记忆增强操作框架（MEMO）。MEMO 构建并维护一个由人类反馈与任务成功记录构成的检索增强型技能库。运行时，它从技能库中检索相关文本与代码，使机器人在推理多任务反馈的同时生成新技能。实验表明，使用 MEMO 将局部反馈聚合为通用技能模板，可有效推广至未见任务，超越现有基线表现。
原文摘要 · Abstract (English)
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
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