机器人不再“做完就算”:每个动作先查前提、后验结果
现在的机器人模型大多只会“预测下一步动作”，但长任务里，动作对了不代表这一步真的有效——它可能撞上障碍、或者做完没达到预期。这篇提出一个框架，把每个技能决策当成“执行提案”:动手前先检查前提是否成立，做完再验证结果是否达成，不通过就恢复重来。关键是这个检查接口是固定的，底层负责动作的模型可以随时换掉，不影响整个循环；每次执行还会留下结构化记录，用来训练和调试各个部件。在 50 个 RoboTwin 任务上平均成功率 86.2%,LIBERO 四套任务 97.4%。但别急着用：它解决的是“怎么把动作模型变成闭环系统”的工程问题，不是让你明天就能部署的成品。
📄 原文摘要(英文)
Vision-language-action (VLA) models map visual observations and language instructions directly to robot actions, but long-horizon tasks require more than action prediction. An agent must coordinate perception, planning, execution, progress verification, and recovery as the physical state evolves. An action prediction or a model-generated skill decision does not, by itself, guarantee that the proposed operation is valid in the current state or that its outcome will be verified. We propose EmbodiedSkills, a unified framework that treats each skill decision as an execution proposal: the runtime checks its prerequisites before execution and verifies the outcome afterward. A shared executable-skill interface connects high-level skill selection, bounded low-level VLA execution, and post-action verification within a single agent loop. Because this interface remains fixed, low-level VLA policies can be replaced or adapted without changing the agent loop. The interface also records planning, execution, verification, and recovery events as structured trajectories, which provide supervision for individual components and can support optional online adaptation when interactive feedback is available. We instantiate EmbodiedSkills with Qwen3-VL and OpenPI/pi0.5 on RoboTwin 2.0 and LIBERO. Task-adapted low-level VLA policies achieve an average success rate of 86.20% across 50 RoboTwin 2.0 tasks and 97.40% across the four LIBERO suites. These results establish the execution performance of the task-adapted low-level VLA policies used in EmbodiedSkills. On four memory-dependent RMBench tasks, the same task-adapted execution approach achieves 12.5% average success. The framework provides a trainable and inspectable agent layer for turning these policies into closed-loop embodied systems.