Faster on-device inference for flow-matching vision-language-action modelsIntermediate
Accelerating VLA Inference with Perturbation Informed Execution
A Seoul National University preprint makes robot foundation models act faster on embedded chips without retraining: it samples a few slightly perturbed action plans per call and executes more steps when the plans agree. On NVIDIA Jetson Orin and Thor this cut inference time by up to 2.3 times while success rates stayed level or rose, including 93.5% against 90.7% on a real UR10e arm.