ActionCodec: What Makes for Good Action Tokenizers
ActionCodec introduces a high-performance action tokenizer guided by information theory to improve Vision-Language-Action (VLA) optimization. It defines design principles for tokens, such as maximizing temporal overlap and minimizing redundancy, and integrates them into an architecture using Residual Vector Quantization. This approach leads to superior training efficiency and robustness across various robotic tasks.