1 citations · 2 across the 8 of their papers we have counts for
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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…