VLA robustnessExpert
Taming VLAs under Robot Execution Errors: Self-Compensation and Stress Testing
This arXiv preprint introduces self-compensating VLA, a deployment-time adaptation method that updates a robot policy online using the residual between commanded and executed motion, plus RoboStress, a simulation benchmark for joint-level execution errors. On two physical Piper arms, it improves average task success by more than 30 percentage points per arm, and on RoboStress it beats base policies and training-time robustness methods. The work matters because mechanical wear, payload, and thermal drift cause real execution errors that standard VLA training does not address.