CARP: Visuomotor Policy Learning via Coarse-to-Fine Autoregressive
  Prediction
Authors: name: Zhefei Gong, name: Pengxiang Ding, name: Shangke Lyu, name: Siteng Huang, name: Mingyang Sun, name: Wei Zhao, name: Zhaoxin Fan, name: Donglin Wang

In robotic visuomotor policy learning, diffusion-based models have achieved
significant success in improving the accuracy of action trajectory generation
compared to traditional autoregressive models. However, they suffer from
inefficiency due to multiple denoising steps and limited flexibility from
complex constraints. In this paper, we introduce Coarse-to-Fine AutoRegressive
Policy (CARP), a novel paradigm for visuomotor policy learning that redefines
the autoregressive action generation process as a coarse-to-fine, next-scale
approach. CARP decouples action generation into two stages: first, an action
autoencoder learns multi-scale representations of the entire action sequence;
then, a GPT-style transformer refines the sequence prediction through a
coarse-to-fine autoregressive process. This straightforward and intuitive
approach produces highly accurate and smooth actions, matching or even
surpassing the performance of diffusion-based policies while maintaining
efficiency on par with autoregressive policies. We conduct extensive
evaluations across diverse settings, including single-task and multi-task
scenarios on state-based and image-based simulation benchmarks, as well as
real-world tasks. CARP achieves competitive success rates, with up to a 10%
improvement, and delivers 10x faster inference compared to state-of-the-art
policies, establishing a high-performance, efficient, and flexible paradigm for
action generation in robotic tasks.