Accelerating VLA Inference with Perturbation Informed Execution

Key points

• Under fixed-horizon execution, the robot executes a fixedlength prefix of each predicted chunk before invoking the policy again, leaving the remaining predictions unused.1
• Extending the execution horizon amortizes policy inference cost over more actions.1
• To address this limitation, we propose PICES (Perturbation-Informed Chunk and Execution Horizon Selection), a training-free method that jointly selects an action chunk and the number of actions executed before the next policy call.1
• Experiments on simulation benchmarks and real-world manipulation tasks demonstrate that PICES achieves speedup by up to 2.3x in wall-clock policy inference time on Jetson Orin and Thor relative to fixed horizon baselines while improving the success rate.1

Sources (1)

• [1]Accelerating VLA Inference with Perturbation Informed Execution
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 06:31 AM

  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.

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