  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 three 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.
