非马尔可夫长时间 horizon 机器人操作 via 关键帧链
Non-Markovian Long-Horizon Robot Manipulation via Keyframe Chaining
AI总结：
本文提出Keyframe-Chaining VLA框架，通过提取和链接关键历史帧，解决长horizon机器人操作中的非马尔可夫依赖问题，提升任务成功率。
摘要：
现有的视觉-语言-动作（VLA）模型往往难以泛化到长horizon任务，因为它们过度依赖即时观察。尽管最近的研究引入了检索机制或扩展上下文窗口来处理程序性任务，但它们往往难以捕捉非马尔可夫依赖性，其中最优动作仅依赖于特定的过去状态，而不是当前观察。为了解决这个问题，我们引入了Keyframe-Chaining VLA，一种框架，它提取并链接关键历史帧以建模长horizon依赖性。具体来说，我们提出了一种自动关键帧选择器，学习了一个判别嵌入空间，有效地识别不同的状态转换。为了捕捉任务关键信息，我们设计了一种进度意识的查询机制，根据其时间相关性动态检索历史帧。这些选定的关键帧被整合到VLA中作为交错的视觉标记，明确将策略根植于长horizon时间上下文中。最后，我们引入了四个基于ManiSkill模拟器的非马尔可夫操作任务，以衡量任务成功率。实验结果表明，我们的方法在处理具有长horizon时间依赖性的机器人操作任务方面表现出色。代码可在https://github.com/cytoplastm/KC-VLA上获得。
英文摘要：
Existing Vision-Language-Action (VLA) models often struggle to generalize to long-horizon tasks due to their heavy reliance on immediate observations. While recent studies incorporate retrieval mechanisms or extend context windows to handle procedural tasks, they often struggle to capture Non-Markovian dependencies, where optimal actions rely solely on specific past states rather than the current observation. To address this, we introduce Keyframe-Chaining VLA, a framework that extracts and links key historical frames to model long-horizon dependencies. Specifically, we propose an automatic keyframe selector that learns a discriminative embedding space, effectively identifying distinct state transitions. To capture task-critical information, we design a progress-aware query mechanism that dynamically retrieves historical frames based on their temporal relevance to the current execution phase. These selected keyframes are integrated into the VLA as interleaved visual tokens, explicitly grounding the policy in the long-horizon temporal context. Finally, we introduce a suite of four Non-Markovian manipulation tasks built upon the ManiSkill simulator to measure task success rates. Experimental results demonstrate that our method achieves superior performance, effectively tackling robot manipulation tasks characterized by long-horizon temporal dependencies. Code is available at https://github.com/cytoplastm/KC-VLA.
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。
