发表机构
Stanford University（斯坦福大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
SpeedTuning是一种轻量强化学习框架，可预测动作最优执行速度，在无需额外数据采集的情况下，将机器人操作策略加速超2.4倍且保持足够成功率，适用于多种动态精确任务。
AI 中文摘要
尽管学习得到的机器人策略有望推动通用操作的发展，但其实际部署常受限于次优的执行速度。模仿学习策略本质上受硬件约束及数据采集阶段操作者速度的限制，且目前尚无成熟的方法可加速通过模仿学习得到的策略，执行速度与任务成功率之间的经验关联也未得到充分探索。为解决这些问题，我们提出SpeedTuning，这是一个专门用于提升操作策略速度的强化学习框架。SpeedTuning学习预测动作的最优执行速度，从而在无需额外数据采集的情况下补充基础策略。我们提供的经验证据表明，SpeedTuning实现了显著的执行速度提升，加速比超过2.4倍，同时相较于原始任务策略及线性插值等简单加速方法，保持了足够的成功率。我们在倾倒、投掷、抓取等多样化的动态及精确任务中评估了该方法，证明了其在增强现实世界机器人操作方面的有效性与鲁棒性。
英文摘要
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 for actions, thereby complementing a base policy without necessitating additional data collection. We provide empirical evidence that SpeedTuning achieves substantial improvements in execution speed, exceeding 2.4x speed-up, while preserving an adequate success rate compared to both the original task policy and straightforward speed-up methods such as linear interpolation at a fixed speed. We evaluate our approach across a diverse set of dynamic and precise tasks, including pouring, throwing, and picking, demonstrating its effectiveness and robustness in enhancing real-world robotic manipulation. Videos and code are available at https://daivdyuan.github.io/speed-tuning/
Comments10 pages, 12 figures. This arXiv version includes an appendix with qualitative simulation rollouts and additional ablations. Published at ICRA 2025
Journal ref2025 IEEE International Conference on Robotics and Automation (ICRA), pp. 1184-1192, 2025
DOI：10.1109/ICRA55743.2025.11128753
