4 papers
Z-1: Efficient Reinforcement Learning for Vision-Language-Action Models
Lang Cao, Renhong Chen, Luyi Li +3
Vision-Language-Action (VLA) models offer a promising framework for robotic manipulation by connecting language instructions, visual observations, and continuous control. However,…
ThinkingVLA: Interleaved Vision and Language Reasoning for Robotic Manipulation
Tianyi Lu, Hui Zhang, Zijie Diao +8
Most Vision-Language-Action (VLA) models map observations directly to actions without explicit reasoning, limiting their capacity for reasoning-intensive long-horizon tasks. To add…
Dense-Jump Flow Matching with Non-Uniform Time Scheduling for Robotic Policies: Mitigating Multi-Step Inference Degradation
Zidong Chen, Zihao Guo, Peng Wang +3
Flow matching has emerged as a competitive framework for learning high-quality generative policies in robotics; however, we find that generalisation arises and saturates early alon…
DINOv3-Diffusion Policy: Self-Supervised Large Visual Model for Visuomotor Diffusion Policy Learning
ThankGod Egbe, Peng Wang, Zhihao Guo +1
This paper evaluates DINOv3, a recent large-scale self-supervised vision backbone, for visuomotor diffusion policy learning in robotic manipulation. We investigate whether a purely…