TempoVLA: Learning Speed-Controllable Vision-Language-Action Policies
Robot manipulation alternates between low-risk transit phases that call for fast execution and high-risk contact stages that demand slow, precise motion. Yet existing Vision-Language-Action models (VLAs) only inherit a single fixed speed from training demonstrations. Prior efforts to accelerate VLAs through model compression, KV-cache reuse, or reinforcement learning only shift the policy from one fixed speed to another, and leave deceleration almost unexplored. We observe that the magnitude of
Record details
Published: 4 June 2026
Source: arXiv
Category: Research
Topics: Regulation · Agents & autonomy
Retrieved: 14 July 2026
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ethics.ai (4 June 2026), “TempoVLA: Learning Speed-Controllable Vision-Language-Action Policies,” evidence record 1408, https://ethics.ai/record/1408 (originally published by arXiv).
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