4 papers
ForceWAM: Decoupling Future and Response Learning from Demo and Play Data for Contact-Rich Manipulation
Zijun Xu, Wendi Chen, Xinkai Wang +16
Expert demonstrations show how to complete contact-rich tasks, but provide limited coverage of how interaction forces respond to different actions. Task-agnostic play provides broa…
AdvDex: Learning Dexterous Manipulation from Human Demonstrations via Joint-Aligned Actions and Adversarial Learning
Zhiyue Zhao, Jingyi Wu, Hairuo Liu +5
Dexterous manipulation is a fundamental capability for embodied intelligence, but scaling it remains difficult because robot demonstrations are expensive to collect and action spac…
Learning Transferable Dynamics Priors from Action to World Modeling
Ze Huang, Jiahui Zhang, Hairuo Liu +3
We study action-conditioned world modeling as a scalable way to learn transferable dynamics priors for robot learning. By pretraining a model to predict how actions drive visual sc…
ForceVLA: Enhancing VLA Models with a Force-aware MoE for Contact-rich Manipulation
Jiawen Yu, Hairuo Liu, Qiaojun Yu +9
Vision-Language-Action (VLA) models have advanced general-purpose robotic manipulation by leveraging pretrained visual and linguistic representations. However, they struggle with c…