5 papers · 1 filter
Progress Reward Modeling for Robotic Learning: A Comprehensive Survey
Jianshu Zhang, Keliang Wu, Haoran Lu +8
Robotic learning takes place in dynamic environments with large behavior spaces. A terminal success signal only tells the robot whether the task is completed. It does not explain w…
Learning to Feel the Future: DreamTacVLA for Contact-Rich Manipulation
Guo Ye, Zexi Zhang, Xu Zhao +4
Vision-Language-Action (VLA) models have shown remarkable generalization by mapping web-scale knowledge to robotic control, yet they remain blind to physical contact. Consequently,…
MagicSim: A Unified Infrastructure for Executable Embodied Interaction
Haoran Lu, Songling Liu, Yue Chen +15
Robot learning and embodied agents now require simulation to serve as a shared execution substrate linking control, skills, and planning, not only as a renderer, controller testbed…
AnnotateAnything: Automatic Annotation of 3D Assets for Robot Manipulation
Haoran Lu, Mutian Shen, Shuyang Yu +9
Simulation enables scalable robot data collection, but raw 3D assets provide only geometry, lacking the semantic, interactive, and physical knowledge needed to specify where and ho…
ACE-Ego-0: Unifying Egocentric Human and Robotic Data for VLA Pretraining
Hao Li, Ganlong Zhao, Yufei Liu +8
Vision-Language-Action (VLA) models benefit from large-scale and diverse embodied data, yet scaling robot trajectory collection is costly and labor-intensive. Recent advances show…