驯服机器人执行错误下的视觉-语言-动作策略：自补偿与压力测试

中文摘要 AI 辅助

当机器人的实际执行运动偏离其指令动作时，视觉-语言-动作（VLA）策略常常会失败。此类执行错误源于机器人的机械特性和运行条件，例如磨损和负载变化。我们提出自补偿VLA，一种部署时的自适应方法，使VLA策略在生成指令时能够预先补偿机器人的执行错误。无需任务奖励或标签，它利用VLA指令的动作与机器人实际执行的运动之间的残差在线更新策略。为了在仅靠物理机器人难以覆盖的执行条件下对VLA鲁棒性进行压力测试，我们引入了RoboStress，一个受控的仿真基准。它结合了已建立的关节级摩擦、间隙、柔顺性和重力补偿误差模型，构建了七个部署场景，这些场景的执行错误依赖于机器人的状态和运动历史。在RoboStress上，自补偿VLA的平均任务成功率高于基础策略和训练中内置鲁棒性的方法。在两个使用历史不同的物理机器人手臂上，它使每条手臂的平均任务成功率提高了超过30个百分点，并且这些增益扩展到了任务演示中未见过的物体。

英文摘要

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.

发表机构

• POSTECH（浦项科技大学）
• MBZUAI（穆罕默德·本·扎耶德人工智能大学）

机构由 AI 辅助整理，请以论文原文为准。