4 papers
Zero-WAM: In-Context World-Action Modeling from Human Videos for Open-Ended Task Generalization
Jiaming Zhou, Qihang Zhang, Gangwei Xu +9
Zero-shot cross-task generalization, where a policy must execute manipulation tasks never seen during training, remains a central challenge in robot learning. In large language mod…
Native Video-Action Pretraining for Generalizable Robot Control
Qihang Zhang, Lin Li, Luyao Zhang +26
The advent of video-action models offers a promising path for robot control. Nevertheless, we argue that repurposing video generative models designed for digital content creation i…
TwinAligner: Visual-Dynamic Alignment Empowers Physics-aware Real2Sim2Real for Robotic Manipulation
Hongwei Fan, Hang Dai, Jiyao Zhang +7
The robotics field is evolving towards data-driven, end-to-end learning, inspired by multimodal large models. However, reliance on expensive real-world data limits progress. Simula…
Real2Edit2Real: Generating Robotic Demonstrations via a 3D Control Interface
Yujie Zhao, Hongwei Fan, Di Chen +5
Recent progress in robot learning has been driven by large-scale datasets and powerful visuomotor policy architectures, yet policy robustness remains limited by the substantial cos…