MatchingPolicy：具备对应感知能力的策略实现跨物体的上下文学习

中文摘要 AI 辅助

上下文模仿学习支持少样本策略泛化，但在未见过的物体和新场景上难以保持性能。为解决该问题，我们提出MatchingPolicy，这是一个由对应关系驱动的框架，明确将演示与场景的匹配从策略学习中解耦。我们方法的核心是一个对应感知的扩散策略，它直接基于密集语义对应关系来条件机器人动作。这种架构分离解决了对应关系识别与动作适应之间的固有冲突，实现了稳健的分布外迁移。我们的框架将视觉基础模型与一种新颖的两阶段匹配算法相结合，以动态建立可靠的对应关系。在RLBench和真实世界操作任务上的广泛评估证实，MatchingPolicy实现了卓越的少样本性能，能在未见过的物体实例和语义类别上可靠泛化。

英文摘要

In-context imitation learning enables few-shot policy generalization but struggles to maintain performance on unseen objects and novel scenarios. To address this, we introduce MatchingPolicy, a correspondence-driven framework that explicitly decouples demonstration-to-scene matching from policy learning. Central to our method is a correspondence-aware diffusion policy that conditions robotic actions directly on dense semantic correspondences. This architectural separation resolves the inherent conflict between correspondence identification and action adaptation, enabling robust out-of-distribution transfer. Our framework integrates vision foundation models with a novel two-stage matching algorithm to dynamically establish reliable correspondences. Extensive evaluations on RLBench and real-world manipulation tasks confirm that MatchingPolicy achieves superior few-shot performance, generalizing reliably across unseen object instances and semantic categories.

发表机构

• Hong Kong University of Science and Technology（香港科技大学）

机构由 AI 辅助整理，请以论文原文为准。