4 papers
RynnVLA-002: A Unified Vision-Language-Action and World Model
Jun Cen, Siteng Huang, Yuqian Yuan +11
We introduce RynnVLA-002, a unified Vision-Language-Action (VLA) and world model. The world model leverages action and visual inputs to predict future image states, learning the un…
MTT-Bench: Predicting Social Dominance in Mice via Multimodal Large Language Models
Yunquan Chen, Haoyu Chen
Understanding social dominance in animal behavior is critical for neuroscience and behavioral studies. In this work, we explore the capability of Multimodal Large Language Models(M…
Adaptive Action Chunking at Inference-time for Vision-Language-Action Models
Yuanchang Liang, Xiaobo Wang, Kai Wang +5
In Vision-Language-Action (VLA) models, action chunking (i.e., executing a sequence of actions without intermediate replanning) is a key technique to improve robotic manipulation a…
WorldVLA: Towards Autoregressive Action World Model
Jun Cen, Chaohui Yu, Hangjie Yuan +9
We present WorldVLA, an autoregressive action world model that unifies action and image understanding and generation. Our WorldVLA intergrates Vision-Language-Action (VLA) model an…