4 papers
Learning 4D Geometric Priors for Inference-Efficient World Action Models
Jianjun Zhang, Jian Zhu, Taiyi Su +4
World Action Models (WAMs) have shown strong potential for robotic manipulation by jointly modeling visual future dynamics and executable action sequences. However, existing video-…
DSWAM: A Dual-System World Action Foundation Model for Fine-Grained Robot Manipulation
Jian Zhu, Jianjun Zhang, Taiyi Su +10
World Action Models (WAMs) provide a promising alternative to Vision-Language-Action (VLA) policies by using video-based world modeling as dense supervision for robot action learni…
DeMaVLA: A Vision-Language-Action Foundation Model for Generalizable Deformable Manipulation
Taiyi Su, Jian Zhu, Tianjian Wang +9
Real-world household robots require Vision-Language-Action (VLA) foundation models that can acquire reusable manipulation skills across diverse objects, task conditions, and househ…
PiL-World: A Chunk-Wise World Model for VLA Policy-in-the-Loop Evaluation
Chong Ma, Taiyi Su, Jian Zhu +4
Vision-language-action (VLA) policies operate in a closed loop in real-world robot tasks: a robot observes the scene, executes an action chunk, and conditions its next decision on…