1 citations · 3 across the 12 of their papers we have counts for
12 papers
CoorGrasp: Coordinated Contact Control for Adaptive Dexterous Grasping Under Uncertainty
Mingrui Yu, Yongpeng Jiang, Yongyi Jia +2
While recent research has focused heavily on dexterous grasp pose generation, less attention has been devoted to the execution of planned grasps. Under shape and position uncertain…
HUGS: Guiding Unified Dexterous Grasp Synthesis Across Modes and Scales via Learned Human Priors
Mingrui Yu, Yongpeng Jiang, Yongyi Jia +4
Dexterous grasping across diverse object scales requires contact modes ranging from two-finger pinches to bimanual grasps. Existing dexterous grasp synthesis methods reduce the hig…
EmbodiSteer: Steering Embodiment-Agnostic Visuomotor Policies with Joint-Space Guidance for Zero-Shot Cross-Embodiment Deployment
Shihefeng Wang, Kangchen Lv, Mingrui Yu +1
Scalable robot imitation learning relies on large-scale heterogeneous data from diverse robots or body-free data, making Cartesian end-effector actions a key interface for embodime…
UniStateDLO: Unified Generative State Estimation and Tracking of Deformable Linear Objects Under Occlusion for Constrained Manipulation
Kangchen Lv, Mingrui Yu, Shihefeng Wang +2
Perception of deformable linear objects (DLOs), such as cables, ropes, and wires, is the cornerstone for successful downstream manipulation. Although vision-based methods have been…
Kinematics-Aware Diffusion Policy with Consistent 3D Observation and Action Space for Whole-Arm Robotic Manipulation
Kangchen Lv, Mingrui Yu, Yongyi Jia +2
Whole-body control of robotic manipulators with awareness of full-arm kinematics is crucial for many manipulation scenarios involving body collision avoidance or body-object intera…
Analyzing Key Objectives in Human-to-Robot Retargeting for Dexterous Manipulation
Chendong Xin, Mingrui Yu, Yongpeng Jiang +2
Kinematic retargeting from human hands to robot hands is essential for transferring dexterity from humans to robots in manipulation teleoperation and imitation learning. However, d…
Robust Model-Based In-Hand Manipulation with Integrated Real-Time Motion-Contact Planning and Tracking
Yongpeng Jiang, Mingrui Yu, Xinghao Zhu +2
Robotic dexterous in-hand manipulation, where multiple fingers dynamically make and break contact, represents a step toward human-like dexterity in real-world robotic applications.…
Robotic In-Hand Manipulation for Large-Range Precise Object Movement: The RGMC Champion Solution
Mingrui Yu, Yongpeng Jiang, Chen Chen +2
In-hand manipulation using multiple dexterous fingers is a critical robotic skill that can reduce the reliance on large arm motions, thereby saving space and energy. This letter fo…
In-Hand Following of Deformable Linear Objects Using Dexterous Fingers with Tactile Sensing
Mingrui Yu, Boyuan Liang, Xiang Zhang +6
Most research on deformable linear object (DLO) manipulation assumes rigid grasping. However, beyond rigid grasping and re-grasping, in-hand following is also an essential skill th…
Contact-Implicit Model Predictive Control for Dexterous In-hand Manipulation: A Long-Horizon and Robust Approach
Yongpeng Jiang, Mingrui Yu, Xinghao Zhu +2
Dexterous in-hand manipulation is an essential skill of production and life. However, the highly stiff and mutable nature of contacts limits real-time contact detection and inferen…
Generalizable whole-body global manipulation of deformable linear objects by dual-arm robot in 3-D constrained environments
Mingrui Yu, Kangchen Lv, Changhao Wang +3
Constrained environments are common in practical applications of manipulating deformable linear objects (DLOs), where movements of both DLOs and robots should be constrained. This…
In-Hand Re-grasp Manipulation with Passive Dynamic Actions via Imitation Learning
Dehao Wei, Guokang Sun, Zeyu Ren +5
Re-grasp manipulation leverages on ergonomic tools to assist humans in accomplishing diverse tasks. In certain scenarios, humans often employ external forces to effortlessly and pr…