1 citations · 1 across the 4 of their papers we have counts for
5 papers
SkeleWAM: Skeleton World-Action Modeling for Efficient Robotic Manipulation
Juyi Sheng, Hua Wang, Mengyuan Liu
World action models (WAMs) combine robot action generation with future state prediction. Existing WAMs typically predict videos or learned visual latents, which represent interacti…
CORE: Common Outcome Regularities from Action-Free Visual Demonstrations for Robot Manipulation
Juyi Sheng, Mingxin Tan, Jincheng Li +1
Robot imitation learning often relies on costly robot demonstrations, while abundant action-free visual demonstrations, such as human videos, are difficult to use because they lack…
Eval-Actions: Fine-Grained Execution Quality Evaluation for Robotic Manipulation
Mengyuan Liu, Juyi Sheng, Peiming Li +4
Although Vision--Action (VA) and Vision--Language--Action (VLA) policies have advanced robotic manipulation, their evaluation remains dominated by binary success rates, which obscu…
MP1: MeanFlow Tames Policy Learning in 1-step for Robotic Manipulation
Juyi Sheng, Ziyi Wang, Peiming Li +1
In robot manipulation, robot learning has become a prevailing approach. However, generative models within this field face a fundamental trade-off between the slow, iterative sampli…
GPA-RAM: Grasp-Pretraining Augmented Robotic Attention Mamba for Spatial Task Learning
Juyi Sheng, Yangjun Liu, Sheng Xu +3
Fine-grained robotic manipulation often fails when inaccurate initial grasps propagate errors and necessitate complex pose correction. We propose Grasp-Pretraining Augmentation (GP…