Overcoming Robot Diffusion Policy Latency: Sub-ms Inference via Action History Initialization
chris_j_paxton · x · 2026-08-06
Diffusion Policy is a core breakthrough driving real-world robot learning, but it suffers from high latency because it computes final action trajectories from random noise.
A new approach initializes the search based on previous actions instead of random noise. This enables incredibly fast policy inference with sub-millisecond latency and improves generalization in many cases, generating high-quality predictions.
Related event: New Research Overcomes Robot Diffusion Policy Latency(2 posts)→
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