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。