生成预测控制：用于动态和难以示范任务的流匹配策略
Generative Predictive Control: Flow Matching Policies for Dynamic and Difficult-to-Demonstrate Tasks
AI总结：
本文提出生成预测控制方法，用于解决动态且难以示范任务中的快速动态问题，通过流匹配策略实现高效反馈和高频率控制。
摘要：
生成控制策略最近在机器人领域取得了重大进展。这些方法通过扩散或流匹配生成动作序列，其训练数据由示范提供。但现有方法有两个关键限制：它们需要专家示范，这可能难以获得，并且它们仅适用于相对缓慢、准静态的任务。在本文中，我们利用采样基于预测控制和生成建模之间的紧密联系，以解决这两个问题。特别是，我们引入了生成预测控制，一种适用于具有快速动态但易于模拟但难以示范的任务的监督学习框架。然后我们展示了如何在推理时间将训练好的流匹配策略进行预热启动，保持时间一致性和实现高频反馈。我们相信生成预测控制为现有的行为克隆方法提供了一种互补的方法，并希望它能为超越准静态示范导向任务的通用策略铺平道路。
英文摘要：
Generative control policies have recently unlocked major progress in robotics. These methods produce action sequences via diffusion or flow matching, with training data provided by demonstrations. But existing methods come with two key limitations: they require expert demonstrations, which can be difficult to obtain, and they are limited to relatively slow, quasi-static tasks. In this paper, we leverage a tight connection between sampling-based predictive control and generative modeling to address each of these issues. In particular, we introduce generative predictive control, a supervised learning framework for tasks with fast dynamics that are easy to simulate but difficult to demonstrate. We then show how trained flow-matching policies can be warm-started at inference time, maintaining temporal consistency and enabling high-frequency feedback. We believe that generative predictive control offers a complementary approach to existing behavior cloning methods, and hope that it paves the way toward generalist policies that extend beyond quasi-static demonstration-oriented tasks.
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。
