1 citations · 1 across the 9 of their papers we have counts for
11 papers
Can World and Action Modeling Benefit Each Other under Unified Causal Training?
Yue Su, Bokai Lin, Xinkai Wang +9
World and action modeling are closely related, yet their reciprocal benefits under unified causal training remain unclear. We introduce OneWM, a simple framework for unified causal…
Video-Arena: A Scalable Agentic Video Benchmark for Spatiotemporal Capability
Kuan Zhang, Yukun Chen, Zhihao Yang +7
Understanding and tracking a dynamic world are fundamental to visual intelligence. Existing video benchmarks provide limited insight into whether models can maintain a coherent und…
SG-WAM: Self-Guided World Modeling in Geometry-Aware Policy Space
Ruiteng Zhao, Zhengshen Zhang, Yue Su +6
World Action Models (WAMs) couple action generation with prediction of future states. Their effectiveness depends on whether future dynamics are modeled in a space that is both ali…
CARA: Concept-Aware Risk Attention for Interpretable Collision Anticipation
Zhishan Tao, Ruoyu Wang, Yucheng Wu +6
Collision anticipation in autonomous driving requires not only accurate early warnings but also interpretable reasoning about what risk factors are being tracked and how risk evolv…
Towards Generalist Game Agents: A Survey of Foundation Models in Virtual Worlds
Kuan Zhang, Dongchen Liu, Qiyue Zhao +12
The real world unfolds along a single set of physics laws, yet human intelligence demonstrates a remarkable capacity to generalize experiences from this singular physical existence…
World Guidance: World Modeling in Condition Space for Action Generation
Yue Su, Sijin Chen, Haixin Shi +7
Leveraging future observation modeling to facilitate action generation presents a promising avenue for enhancing the capabilities of Vision-Language-Action (VLA) models. However, e…
CLAP: Contrastive Latent Action Pretraining for Learning Vision-Language-Action Models from Human Videos
Chubin Zhang, Jianan Wang, Zifeng Gao +5
Generalist Vision-Language-Action models remain constrained by the scarcity of robotic data relative to the abundance of human video demonstrations. Existing Latent Action Models a…
DSPv2: Improved Dense Policy for Effective and Generalizable Whole-body Mobile Manipulation
Yue Su, Chubin Zhang, Sijin Chen +4
Learning whole-body mobile manipulation via imitation is essential for generalizing robotic skills to diverse environments and complex tasks. However, this goal is hindered by sign…
Dense Policy: Bidirectional Autoregressive Learning of Actions
Yue Su, Xinyu Zhan, Hongjie Fang +5
Mainstream visuomotor policies predominantly rely on generative models for holistic action prediction, while current autoregressive policies, predicting the next token or chunk, ha…
Motion Before Action: Diffusing Object Motion as Manipulation Condition
Yue Su, Xinyu Zhan, Hongjie Fang +3
Inferring object motion representations from observations enhances the performance of robotic manipulation tasks. This paper introduces a new paradigm for robot imitation learning…
Generative Adversarial Patches for Physical Attacks on Cross-Modal Pedestrian Re-Identification
Yue Su, Hao Li, Maoguo Gong
Visible-infrared pedestrian Re-identification (VI-ReID) aims to match pedestrian images captured by infrared cameras and visible cameras. However, VI-ReID, like other traditional c…