机器人手抖，AI 自己学会了补刀
机器人执行动作时总会跑偏——机械磨损、负载变化都会让实际动作偏离 AI 下的指令，而 VLA 模型(把视觉、语言、动作连起来的机器人 AI)一遇到这种偏差就翻车。这篇提出一个部署时的自适应方法：不用重新训练，AI 在干活时实时对比「自己下的指令」和「机器人实际做的动作」，算出差值，下次下指令时提前把这个差值补回去。相当于 AI 自己学会了给机器人的手抖做预补偿。在模拟基准 RoboStress 和两台不同磨损程度的真实机械臂上，任务成功率平均提升超过 30 个百分点，连没见过的物体也能受益。它不是你明天就能装进自家机器人的东西，但指向一个更省事的方向：与其花大力气让机器人更精准，不如让 AI 学会适应机器人的不精准。
📄 原文摘要(英文)
Vision-language-action (VLA) policies often fail when a robot's executed motion deviates from their commanded action. Such execution errors arise from the robot's mechanics and operating conditions, such as wear and payload changes. We propose self-compensating VLA, a deployment-time adaptation method that enables a VLA policy to pre-compensate for the robot's execution errors when generating commands. Without task rewards or labels, it updates the policy online using the residual between the action commanded by a VLA and the motion executed by the robot. To stress-test VLA robustness across execution conditions that are impractical to cover with physical robots alone, we introduce RoboStress, a controlled simulation benchmark. It combines established joint-level models of friction, backlash, compliance, and gravity-compensation error into seven deployment scenarios whose execution errors depend on the robot's state and motion history. On RoboStress, self-compensating VLA achieves higher average task success than both the base policies and methods that build in robustness during training. On two physical robot arms with different usage histories, it raises the average task success rate by more than 30 percentage points on each arm, and the gains extend to objects not seen in the task demonstrations.