Leveraging prior knowledge from pretrained policies, foundation models, or human operators offers an efficient alternative to learning robot skills from scratch. However, these agents often provide actions that are suboptimal, noisy, or misaligned with task-specific expert behavior. We propose GLOVES, a family of flow-based adaptation methods that correct non-expert actions by transporting them toward an expert action distribution. Rather than replacing agentic control with full autonomy, GLOVES performs selective action-level adaptation, improving task success while preserving agent intent. The learned flow also provides a natural in-distribution scoring mechanism through reverse flow evaluation. We use this signal as an intervention gate: actions that appear consistent with the expert distribution are passed through unchanged, while anomalous or out-of-distribution (OOD) actions are corrected. In this way, assistance is only provided when necessary. GLOVES requires only limited expert supervision, using a small number of demonstrations or reusable successful skill segments. By learning local expert action patterns and stitching them during execution, GLOVES provides a lightweight shared-control module for robust action adaptation across tasks and environments. Code and demos are available at ripl.github.io/GLOVES_web.

翻译：利用预训练策略、基础模型或人类操作员提供的先验知识，是从零开始学习机器人技能的高效替代方案。然而，这些智能体提供的动作往往存在次优、含噪声或与特定任务专家行为不匹配的问题。我们提出GLOVES——一类基于流的自适应方法，通过将非专家动作向专家动作分布迁移实现纠正。GLOVES并非以完全自主控制取代智能体决策，而是执行选择性的动作级自适应，在提升任务成功率的同时保留智能体意图。通过学习得到的流，还可通过逆向流评估提供天然的同分布评分机制。我们利用该信号作为干预门控：与专家分布一致的动作被原样通过，异常或分布外（OOD）动作则被纠正。通过这种方式，仅在必要时提供辅助。GLOVES只需有限的专家监督，使用少量演示或可复用的成功技能片段。通过学习局部专家动作模式并在执行过程中进行拼接，GLOVES提供轻量级共享控制模块，实现跨任务与环境的鲁棒动作自适应。代码与演示见ripl.github.io/GLOVES_web。