From fisherman to sushi tycoon. ๐ฃ
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Why do VLAs move so slowly? They copy cautious teleoperators, even in free space where nothing can go wrong.
PaceVLA (KAIST, RLWRLD, Korea University, SNU) lets the policy pick its pace for every action chunk. Four decoders on a shared action expert (GR00T N1.5) output either normal actions or coarse ones that merge pairs of steps, each with a short or long horizon. A small judge decides when coarse is safe: in free space yes, around grasping and contact no.
The judge learns from per-step labels written by VLMs: Claude Opus 5 helped design the criteria from how fixed-speed policies failed, and Gemini 3.8 Flash then rated every demonstration chunk.
โข Real robots: 33โ45% shorter execution on a Franka and a bimanual OpenArm with dexterous hands at similar success, e.g. Air Fryer 24.6 โ 14.6 s at 81% both times; on the ALLEX humanoid, 6.3 instead of 3.6 packages per minute, with success up from 86% to 94%
โข Baselines: fixed quantization, DemoSpeedUp and SAIL are also faster but lose up to 39 points of success on the real tasks
โข Simulation: smaller gains, mostly 1.1โ1.3ร effective speedup on RoboCasa, LIBERO and DexJoCo
โข Fine print: 1.05ร inference overhead, and the controller gains had to be raised to track the larger steps
I like code as engine and diffusion as renderer, but I'm skeptical about ๐ฑ๐ฒ๐ณ๐ผ๐ฟ๐บ๐ฎ๐ฏ๐น๐ฒ part through code, which is hard even in traditional 3D pipelines.
showcase a 8B pure diffusion model (https://t.co/KPTYQO2KMk) doing something similar at a fraction of the cost and time
Steady, aim, duplicate!
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"generalist contact planner + a generalist tracker + a generalized neural mapping"
This, btw, is a big deal and I can't think of any other recent paper that does this on a humanoid
Next steps from here?
- arbitrary contacts, not predefined set
- unified model that does all
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