Cybo-Waiter：一个人形全身体态运动- manipulation的物理代理框架
Cybo-Waiter: A Physical Agentic Framework for Humanoid Whole-Body Locomotion-Manipulation
AI总结：
Cybo-Waiter提出了一种人形全身体态运动-操作的物理代理框架，通过多对象3D几何监督和条件诊断提升任务执行的鲁棒性与稳定性。
摘要：
机器人越来越被期望在人类环境中执行开放式的自然语言请求，这要求在部分可观测性下实现可靠的长周期执行。这对人形机器人来说尤其具有挑战性，因为运动和操作通过姿态、可达性和平衡紧密耦合。我们提出了一个人形代理框架，将VLM计划转化为可验证的任务程序，并通过多对象3D几何监督闭合回路。一个VLM规划器将每个指令编译成一个带有显式基于谓词的先决条件和成功条件的类型JSON子任务序列。使用SAM3和RGB-D，我们将所有相关任务实体定位在3D空间中，估计物体质心和范围，并在稳定的帧上评估谓词以获得条件层面的诊断。监督器利用这些诊断来验证子任务的完成，并为进展和重新规划提供条件层面的反馈。我们通过协调人形运动和全身操作执行每个子任务，选择在可达性和平衡约束下可行的运动基元。在桌面操作和长周期人形运动-操作任务上的实验表明，多对象定位、时间稳定性以及由恢复驱动的重新规划提高了鲁棒性。
英文摘要：
Robots are increasingly expected to execute open ended natural language requests in human environments, which demands reliable long horizon execution under partial observability. This is especially challenging for humanoids because locomotion and manipulation are tightly coupled through stance, reachability, and balance. We present a humanoid agent framework that turns VLM plans into verifiable task programs and closes the loop with multi object 3D geometric supervision. A VLM planner compiles each instruction into a typed JSON sequence of subtasks with explicit predicate based preconditions and success conditions. Using SAM3 and RGB-D, we ground all task relevant entities in 3D, estimate object centroids and extents, and evaluate predicates over stable frames to obtain condition level diagnostics. The supervisor uses these diagnostics to verify subtask completion and to provide condition-level feedback for progression and replanning. We execute each subtask by coordinating humanoid locomotion and whole-body manipulation, selecting feasible motion primitives under reachability and balance constraints. Experiments on tabletop manipulation and long horizon humanoid loco manipulation tasks show improved robustness from multi object grounding, temporal stability, and recovery driven replanning.
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