2 citations · 3 across the 7 of their papers we have counts for
9 papers
Outcome-Sensitive Motion Search for Impact-Aware Dexterous Catching
Guorui Pei, Jinsong Wu, Songyuan Su +5
Skilled humans can catch fast-moving objects softly by coordinating interception, velocity matching, and follow-through to mitigate impact. Learning such impact-aware catching with…
World Models for Robotic Manipulation: A Survey
Fangyuan Wang, Ziyuan Wang, Guorui Pei +15
Robotic manipulation depends on the ability to anticipate how actions reshape objects, contacts, and scene geometry before execution. Learned world models provide this capability b…
Learning a Kinodynamic Trajectory Manifold for Impact-Aware Compliant Catching of Fast-Moving Objects
Guorui Pei, Mengshi Zhang, Xi Chen +3
Fast catching of free-flying objects is difficult because of short reaction time, impact uncertainty, and kinodynamic constraints. We use reinforcement learning in simulation to co…
Think Proprioceptively: State-Grounded Visual Token Selection for VLA Policies
Fangyuan Wang, Peng Zhou, Jiaming Qi +4
Vision-language-action (VLA) models typically inject proprioception only as a late conditioning signal, preventing robot state from grounding instruction understanding or directing…
Failure-Aware Bimanual Teleoperation via Conservative Value Guided Assistance
Peng Zhou, Zhongxuan Li, Jinsong Wu +7
Teleoperation of high-precision manipulation is con-strained by tight success tolerances and complex contact dy-namics, which make impending failures difficult for human operators…
BagIt! An Adaptive Dual-Arm Manipulation of Fabric Bags for Object Bagging
Peng Zhou, Jiaming Qi, Hongmin Wu +3
Bagging tasks, commonly found in industrial scenarios, are challenging considering deformable bags' complicated and unpredictable nature. This paper presents an automated bagging s…
Revolutionizing Packaging: A Robotic Bagging Pipeline with Constraint-aware Structure-of-Interest Planning
Jiaming Qi, Peng Zhou, Pai Zheng +4
Bagging operations, common in packaging and assisted living applications, are challenging due to a bag's complex deformable properties. To address this, we develop a robotic system…
Bimanual Deformable Bag Manipulation Using a Structure-of-Interest Based Neural Dynamics Model
Peng Zhou, Pai Zheng, Jiaming Qi +5
The manipulation of deformable objects by robotic systems presents a significant challenge due to their complex and infinite-dimensional configuration spaces. This paper introduces…
Integrating Visual Foundation Models for Enhanced Robot Manipulation and Motion Planning: A Layered Approach
Chen Yang, Peng Zhou, Jiaming Qi
This paper presents a novel layered framework that integrates visual foundation models to improve robot manipulation tasks and motion planning. The framework consists of five layer…