PaceVLA: Adaptive Temporal Action Quantization in Vision-Language-Action Models

Key points

• Fast and reliable manipulation is crucial for practical robot deployment.1
• Although Vision-Language-Action (VLA) models have recently shown remarkable progress as generalist robot policies, their execution often remains temporally inefficient.1
• Temporal action quantization offers a simple way to accelerate execution by aggregating consecutive low-level actions into temporally coarser actions.1
• Experiments across three multi-task simulation benchmarks and six real-world tasks show that PaceVLA improves execution efficiency while maintaining task success rate across multiple embodiments, including long-horizon and dexterous manipulation.1

Sources (1)

• [1]PaceVLA: Adaptive Temporal Action Quantization in Vision-Language-Action Models
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 07:54 AM

  However, applying action quantization without considering when fine-grained control is required introduces a speed-accuracy trade-off: accelerated actions may efficiently reduce redundant steps in some phases, while discarding critical motion details in precision-sensitive interactions.

  Fast and reliable manipulation is crucial for practical robot deployment.

Extractive summary: sentences quoted from the sources.