  Sequence modeling approaches have shown promising results in robot imitation
learning. Recently, diffusion models have been adopted for behavioral cloning
in a sequence modeling fashion, benefiting from their exceptional capabilities
in modeling complex data distributions. The standard diffusion-based policy
iteratively generates action sequences from random noise conditioned on the
input states. Nonetheless, the model for diffusion policy can be further
improved in terms of visual representations. In this work, we propose Crossway
Diffusion, a simple yet effective method to enhance diffusion-based visuomotor
policy learning via a carefully designed state decoder and an auxiliary
self-supervised learning (SSL) objective. The state decoder reconstructs raw
image pixels and other state information from the intermediate representations
of the reverse diffusion process. The whole model is jointly optimized by the
SSL objective and the original diffusion loss. Our experiments demonstrate the
effectiveness of Crossway Diffusion in various simulated and real-world robot
tasks, confirming its consistent advantages over the standard diffusion-based
policy and substantial improvements over the baselines.
