Taming VLAs under Robot Execution Errors: Self-Compensation and Stress Testing

• 类型：arxiv
• 标识：2609.37334
• 链接：https://arxiv.org/abs/2609.37334
• 主分类：multimodal
• 形态：application
• TLDR：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 e
• 待LLM分类：否
• 来源文件：
• /inbox/tom/_candidates/2026-10-07-agent-rag-longcontext-candidates.json