Xiaokang Yang

M2Tok: Multi-head Multi-codebook Discrete Action Tokenization for Vision-Language-Action Models

Recent advancements have successfully adapted autoregressive language models to process multimodal signals, such as images and actions. Since raw action signals are continuous, effective tokenization is essential to map high-dimensional inputs into compact discrete tokens for autoregressive processing. However, existin...

MoEActok: A MoE-based Action Tokenizer for Vision-Language-Action Models

Enfold: Folding World Model Imagination into Predictive Representations for Ultra-Efficient Embodied Control

This work presents Enfold, which transfers this computation that constructs a future into a representation predicted from the current visual context and language instruction, and recast a world generator as a source of predictive control representations if its internal structure can be enfolded into the present.