4 papers
TS-WAM: Temporal Latent Actions and World-Action Coupling for Vision-Language Robot Control
Menghao Yin, Jiaju Yin, Xiyue Dong +5
Many vision-language-action policies rely on action-labeled robot demonstrations and lack an explicit mechanism for predicting future visual states. These limitations hinder learni…
Memorize, Adapt, Ignore: Diagnosing Robot Learning Mechanisms under Training Data Variation
Ke Zhang, Danica J. Sutherland, Chao Liu
Training data variation, whether through designing a domain randomization (DR) scheme in simulation or curating demonstrations for imitation learning, is a primary lever for improv…
DexFormer: Cross-Embodied Dexterous Manipulation via History-Conditioned Transformer
Ke Zhang, Lixin Xu, Chengyi Song +4
Dexterous manipulation remains one of the most challenging problems in robotics, requiring coherent control of high-DoF hands and arms under complex, contact-rich dynamics. A major…
RLDG: Robotic Generalist Policy Distillation via Reinforcement Learning
Charles Xu, Qiyang Li, Jianlan Luo +1
Recent advances in robotic foundation models have enabled the development of generalist policies that can adapt to diverse tasks. While these models show impressive flexibility, th…