非同步機器人推論：解耦動作預測與執行
Original: Asynchronous Robot Inference: Decoupling Action Prediction and Execution
In the fields of robot learning and embodied AI, enabling controllers based on deep learning or large language/vision models (VLAs) to run…
傳統機器人控制常受限於 AI 模型推論速度，導致動作不流暢。Hugging Face 提出「非同步機器人推論」架構，將「動作預測（AI 模型）」與「動作執行（硬體控制）」解耦。此方法允許硬體以高頻率（如 100Hz+）持續運行，而較慢的 AI 模型則在背景非同步更新動作指令，大幅提升了機器人在實時環境中的反應速度與操作流暢度。
In the fields of robot learning and embodied AI, enabling controllers based on deep learning or large language/vision models (VLAs) to run in real time has always been a significant challenge. Traditional synchronous control loops require a robot to complete steps sequentially: "sense the environment → run model inference → execute action." However, modern neural networks — such as Diffusion Policy or large vision-language-action models — typically have long and unpredictable inference times, which causes robotic movements to stutter, jitter, or even fail entirely due to excessive latency.
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