7 papers
G0.5: One Autoregressive Stream for Robot Reasoning and Action
Yicheng Liu, Zibin Dong, Baijun Ye +24
The prevailing recipe for Vision-Language-Action (VLA) models couples a pretrained VLM with a separately trained flow-matching action expert. This makes the VLM a context encoder r…
ActionCodec: What Makes for Good Action Tokenizers
Zibin Dong, Yicheng Liu, Shiduo Zhang +8
Vision-Language-Action (VLA) models leveraging the native autoregressive paradigm of Vision-Language Models (VLMs) have demonstrated superior instruction-following and training eff…
TTT-Parkour: Rapid Test-Time Training for Perceptive Robot Parkour
Shaoting Zhu, Baijun Ye, Jiaxuan Wang +5
Achieving highly dynamic humanoid parkour on unseen, complex terrains remains a challenge in robotics. Although general locomotion policies demonstrate capabilities across broad te…
FASTer: Toward Efficient Autoregressive Vision Language Action Modeling via Neural Action Tokenization
Yicheng Liu, Shiduo Zhang, Zibin Dong +12
Autoregressive vision-language-action (VLA) models have recently demonstrated strong capabilities in robotic manipulation. However, their core process of action tokenization often…
GS-Occ3D: Scaling Vision-only Occupancy Reconstruction with Gaussian Splatting
Baijun Ye, Minghui Qin, Saining Zhang +7
Occupancy is crucial for autonomous driving, providing essential geometric priors for perception and planning. However, existing methods predominantly rely on LiDAR-based occupancy…
CRUISE: Cooperative Reconstruction and Editing in V2X Scenarios using Gaussian Splatting
Haoran Xu, Saining Zhang, Peishuo Li +15
Vehicle-to-everything (V2X) communication plays a crucial role in autonomous driving, enabling cooperation between vehicles and infrastructure. While simulation has significantly c…