CAP: Codebook-Aligned Prediction for Tokenized Robot Policies

This research proposes CAP, a method that reuses the code vectors from a tokenizer as policy class prototypes, improving task success in both simulation and real-robot tasks. The approach maintains the tokenizer and policy backbone unchanged, showing that the latent structure of tokenizers provides valuable information for policy learning.