DEFLECT: Temporal Counterfactual Preference Learning for Delay-Robust Asynchronous VLAs 事件

DEFLECT: Temporal Counterfactual Preference Learning for Delay-Robust Asynchronous VLAs arXiv:2605.19294v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) policies increasingly rely on asynchronous inference to hide large-model latency behind ongoing robot motion. While this avoids the stop-and-go behavior of synchronous action-chunk execution, it creates a prediction-execution mismatch: the next chunk is computed from a stale observation at inference start but executed only