VLALagBench: Understanding When and Why VLA Inference Latency Matters
Overview
This paper introduces VLALagBench, a benchmark for evaluating Vision-Language-Action (VLA) models under varying inference latencies. The study reveals that even a 100 ms latency can nearly halve task success on moving targets, with sensitivity dependent on task type and policy. The authors also develop an empirical model to predict task success from limited calibration data, achieving prediction errors between 5.7% and 7.8%, which can guide system-specific configuration choices for maximizing performance.
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“cy sensitivity across task types, object speeds, execution modes (synchronous and asynchronous), and VLA policies. Using VLALagBench, we find that even a modest inference latency of 100 ms can nearly halve task success on moving targets, with sensitivity varying substantially across tasks and policies. These differences reflect task-specific requirements for precision and continuous motion. By overlapping inference w”
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- arXiv Robotics — research abstractsSignalVLALagBench: Understanding When and Why VLA Inference Latency Matters
This paper introduces VLALagBench, a benchmark for evaluating Vision-Language-Action (VLA) models under varying inference latencies. The study reveals that even a 100 ms latency can nearly halve task success on moving targets, with sensitivity dependent on task type and policy. The authors also develop an empirical model to predict task success from limited calibration data, achieving prediction errors between 5.7% and 7.8%, which can guide system-specific configuration choices for maximizing performance.
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