任务达成身体运动定律：机器人操作动作成功的公理化规范
The Law of Task-Achieving Body Motion: Axiomatizing Success of Robot Manipulation Actions
AI总结：
本文提出任务达成身体运动定律，通过公理化规范确保机器人操作动作在语义、因果和可行性方面的正确性，支持运动合成与故障诊断。
摘要：
自主执行日常操作动作的智能体需要确保其身体运动在任务请求上具有语义正确性，在其环境中具有因果有效性，并且在其自身结构上是可行的。为了使机器人能够验证这些属性，我们引入了任务达成身体运动定律作为身体运动的公理化正确性规范。为此，我们引入了具有范围的任务-环境-结构（TEE）类，用以表示世界状态作为语义数字双胞胎（SDTs），并定义适用的物理模型，将任务完成分解为三个谓词：SatisfiesRequest用于在SDT状态演变上对语义请求的满足；Causes用于在具有范围的物理模型下进行因果充分性；CanPerform用于在结构层面进行安全性和可行性验证。这种分解产生了一个可重用、与实现无关的接口，支持运动合成和给定身体运动的验证。它还支持类型化的故障诊断（语义、因果、结构和超范围）、跨机器人和环境的可行性，以及对机器人身体运动的反事实推理。我们通过将其实例化为厨房环境中机械臂容器操作来演示该定律的实际可用性，该实例在三种对比的移动操作平台上进行。
英文摘要：
Autonomous agents that perform everyday manipulation actions need to ensure that their body motions are semantically correct with respect to a task request, causally effective within their environment, and feasible for their embodiment. In order to enable robots to verify these properties, we introduce the Law of Task-Achieving Body Motion as an axiomatic correctness specification for body motions. To that end we introduce scoped Task-Environment-Embodiment (TEE) classes that represent world states as Semantic Digital Twins (SDTs) and define applicable physics models to decompose task achievement into three predicates: SatisfiesRequest for semantic request satisfaction over SDT state evolution; Causes for causal sufficiency under the scoped physics model; and CanPerform for safety and feasibility verification at the embodiment level. This decomposition yields a reusable, implementation-independent interface that supports motion synthesis and the verification of given body motions. It also supports typed failure diagnosis (semantic, causal, embodiment and out-of-scope), feasibility across robots and environments, and counterfactual reasoning about robot body motions. We demonstrate the usability of the law in practice by instantiating it for articulated container manipulation in kitchen environments on three contrasting mobile manipulation platforms
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