SpeedTuning: Speeding Up Policy Execution with Lightweight Reinforcement Learning
While learned robotic policies hold promise for advancing generalizable manipulation, their practical deployment is often hindered by suboptimal execution speeds. Imitation learning policies are inherently limited by hardware constraints and the speed of the operator during data collection. In addition, there are no established methods for accelerating policies learned via imitation, and the empirical relationship between execution speed and task success remains underexplored. To address these issues, we introduce SpeedTuning, a reinforcement learning framework specifically designed to enhance the speed of manipulation policies. SpeedTuning learns to predict the optimal execution speed fo...
| Authors | David D. Yuan, Tony Z. Zhao, Kaylee Burns et al. |
|---|---|
| Primary category | cs.RO |
| Published | 10 August 2026 |
| Last updated at source | 11 August 2026 |
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