Accelerating VLA Inference with Perturbation Informed Execution

Action-chunking vision-language-action (VLA) models predict sequences of future robot actions. Under fixed-horizon execution, the robot executes a fixedlength prefix of each predicted chunk before invoking the policy again, leaving the remaining predictions unused.

From the abstract. Our summary is in progress.