Many robotic systems, such as mobile manipulators or quadrotors, cannot be equipped with high-end GPUs due to space, weight, and power constraints. These constraints prevent these systems from leveraging recent developments in visuomotor policy architectures that require high-end GPUs to achieve fast policy inference. In this paper, we propose Consistency Policy, a faster and similarly powerful alternative to Diffusion Policy for learning visuomotor robot control. By virtue of its fast inference speed, Consistency Policy can enable low latency decision making in resource-constrained robotic setups. A Consistency Policy is distilled from a pretrained Diffusion Policy by enforcing self-consistency along the Diffusion Policy's learned trajectories. We compare Consistency Policy with Diffusion Policy and other related speed-up methods across 6 simulation tasks as well as two real-world tasks where we demonstrate inference on a laptop GPU. For all these tasks, Consistency Policy speeds up inference by an order of magnitude compared to the fastest alternative method and maintains competitive success rates. We also show that the Conistency Policy training procedure is robust to the pretrained Diffusion Policy's quality, a useful result that helps practioners avoid extensive testing of the pretrained model. Key design decisions that enabled this performance are the choice of consistency objective, reduced initial sample variance, and the choice of preset chaining steps. Code and training details will be released publicly.

通过一项快速推断的Consistency Policy方法，本研究提出了一种在资源受限的机器人系统中实现低延迟决策的有效替代Diffusion Policy的学习视觉动作控制方法。通过在已训练的Diffusion Policy中强制实施自我一致性，从而获得Consistency Policy，并在六个仿真任务和两个真实世界任务上与Diffusion Policy和其他相关加速方法进行比较，结果显示Consistency Policy相比其他方法可以提高一个数量级的推断速度并保持竞争性的成功率。

一致性策略：通过一致性蒸馏加速视觉动作策略