两篇新的研究论文介绍了一种使具身代理能够执行长时程操作任务的框架。Cortex 使用一种双向对齐的具身代理框架，并带有定制的规划接口，将高层视觉语言模型（VLMs）的可执行子任务计划传达给低层视觉语言动作（VLA）模型。ACE 是另一个框架，它采用零样本工作流推理来进行桌面操作，将代理推理与可执行技能以及用于适应动态环境和执行失败的多时间尺度记忆相结合。这两种方法都旨在克服当前模型在处理复杂、多步任务方面的局限性。 AI
影响 这些框架通过实现更复杂、更长时程的操作任务，推动了具身AI的发展，有望带来更强大的机器人系统。
排序理由 arXiv 上发表了两篇研究论文，介绍了用于具身代理的新框架。
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arXiv:2607.05377v1 Announce Type: cross Abstract: While recent Vision-Language-Action (VLA) models show promise toward generalist manipulation policies, they struggle with long-horizon tasks due to their Markovian nature-relying solely on current observations. Hierarchical dual-s…
arXiv:2607.04162v1 Announce Type: cross Abstract: Open-ended tabletop manipulation requires agents to not only understand natural language but also adapt to dynamic environments and execution failures. We present ACE (Agentic Control for Embodied Manipulation), a zero-shot workfl…
While recent Vision-Language-Action (VLA) models show promise toward generalist manipulation policies, they struggle with long-horizon tasks due to their Markovian nature-relying solely on current observations. Hierarchical dual-system methods address this but suffer from a gap b…