SOURCE-LINKED INTELLIGENCE

SmoothRL: Online Reinforcement Learning During Asynchronous Execution

arXiv · AI, language, vision and robotics · article · Aug 30, 2026 · UTC

Deploying robot policies in the physical world requires satisfying two fundamental desiderata: reliability and smooth real-time execution. However, deploying state-of-the-art generalist models presents challenges on both fronts. Achieving the precision and robustness required for real-world deployment necessitates sample-efficient online reinforcement learning (RL) to adapt pretrained models. Meanwhile, the increasing scale of robot foundation models has led to higher inference latency. To satisfy real-time constraints under high latency, modern systems adopt asynchronous inference with action

Read original source ↗ Open in workspace

recordType

paper

region

Global