If you didn't yet, you should read the technical report for SmolVLA, published yesterday by the Hugging Face robotics team!
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If you didn't yet, you should read the technical report for SmolVLA, published yesterday by the Hugging Face robotics team!
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➡️ Amongst other ideas, it introduces "Async inference" to boost their robot actions.
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➡️ Amongst other ideas, it introduces "Async inference" to boost their robot actions.
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Robots have a problem: performing the actions takes time (Unlike agents where action executions are near-instant!)
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Robots have a problem: performing the actions takes time (Unlike agents where action executions are near-instant!)
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Most often, robots wait until they've finished performing actions to start thinking about hte next steps. This is a huge latency cost!
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Most often, robots wait until they've finished performing actions to start thinking about hte next steps. This is a huge latency cost!
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So the team decided to have the PolicyServer (aka the"thinking" part) restart early : instead of waiting for the n observations they just sent to be completed, they gather the observations after k < n steps, and start preparing the next actions based on that while the steps are running until n, to directly send their next steps.
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So the team decided to have the PolicyServer (aka the"thinking" part) restart early : instead of waiting for the n observations they just sent to be completed, they gather the observations after k < n steps, and start preparing the next actions based on that while the steps are running until n, to directly send their next steps.
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➡️ This boosted robot throughput by ~30%! (nearly 2× tasks per time window).
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➡️ This boosted robot throughput by ~30%! (nearly 2× tasks per time window).
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gg 
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gg 
@cadene
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cadene
 and team! 👏
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 and team! 👏
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Report here: 
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Report here: 
SmolVLA: A Vision-Language-Action Model for Affordable and Efficient
  Robotics (2506.01844)
https://huggingface.co/papers/2506.01844
2506.01844
paper
resource
https://huggingface.co/papers/2506.01844
If you didn't yet, you should read the technical report for SmolVLA, published yesterday by the Hugging Face robotics team!
➡️ Amongst other ideas, it introduces "Async inference" to boost their robot actions.

Robots have a problem: performing the actions takes time (Unlike agents where action executions are near-instant!)
Most often, robots wait until they've finished performing actions to start thinking about hte next steps. This is a huge latency cost!

So the team decided to have the PolicyServer (aka the"thinking" part) restart early : instead of waiting for the n observations they just sent to be completed, they gather the observations after k < n steps, and start preparing the next actions based on that while the steps are running until n, to directly send their next steps.

➡️ This boosted robot throughput by ~30%! (nearly 2× tasks per time window).

gg @cadene and team! 👏

Report here: https://huggingface.co/papers/2506.01844
🚀
victor John6666 TungzTwisted ocano
🔥
m1b