87 citations · 102 across the 19 of their papers we have counts for
22 papers
A Reconfigurable Dual-Opposition Architecture for Single-Hand Assembly and Manipulation
William Su, Yunosuke Nakamura, Yixiao Wang +6
In-hand assembly is constrained by the need to maintain grasps on two separate parts while controlling their relative motion within a single hand. To enable both in-hand assembly a…
Arm-Aware Guided Dexterous Grasp Generation with Arm-Agnostic Grasp Models
Yongyi Jia, Yongpeng Jiang, Kangchen Lv +3
Dexterous grasp generation that considers arm-related constraints is crucial in real-world scenarios involving arm environment collision avoidance, workspace boundary grasps, and c…
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…
Towards Human-level Dexterous Teleoperation
Puhao Li, Zeyuan Chen, Yingying Wu +9
Humans routinely wield tools, swap grasps, and reposition objects within a single hand, seamlessly orchestrating contact transitions that span translation, reorientation, and finge…
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…
UniLab: A Heterogeneous Architecture for Robot RL Beyond GPU-Dominant Paradigms
Yufei Jia, Zhanxiang Cao, Mingrui Yu +48
Simulation-based RL for contemporary robot control is increasingly organized around GPU-resident simulation: physics, rollout collection, and learning are placed on a single GPU-ce…
GS-Playground: A High-Throughput Photorealistic Simulator for Vision-Informed Robot Learning
Yufei Jia, Heng Zhang, Ziheng Zhang +39
Embodied AI research is undergoing a shift toward vision-centric perceptual paradigms. While massively parallel simulators have catalyzed breakthroughs in proprioception-based loco…
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…
A Unified Interaction Control Framework for Safe Robotic Ultrasound Scanning with Human-Intention-Aware Compliance
Xiangjie Yan, Shaqi Luo, Yongpeng Jiang +6
The ultrasound scanning robot operates in environments where frequent human-robot interactions occur. Most existing control methods for ultrasound scanning address only one specifi…
Visual Attention Based Cognitive Human-Robot Collaboration for Pedicle Screw Placement in Robot-Assisted Orthopedic Surgery
Chen Chen, Qikai Zou, Yuhang Song +4
Current orthopedic robotic systems largely focus on navigation, aiding surgeons in positioning a guiding tube but still requiring manual drilling and screw placement. The automatio…
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…
A Coarse-to-Fine Framework for Dual-Arm Manipulation of Deformable Linear Objects with Whole-Body Obstacle Avoidance
Mingrui Yu, Kangchen Lv, Changhao Wang +2
Manipulating deformable linear objects (DLOs) to achieve desired shapes in constrained environments with obstacles is a meaningful but challenging task. Global planning is necessar…
Global Model Learning for Large Deformation Control of Elastic Deformable Linear Objects: An Efficient and Adaptive Approach
Mingrui Yu, Kangchen Lv, Hanzhong Zhong +2
Robotic manipulation of deformable linear objects (DLOs) has broad application prospects in many fields. However, a key issue is to obtain the exact deformation models (i.e., how r…
Shape Control of Deformable Linear Objects with Offline and Online Learning of Local Linear Deformation Models
Mingrui Yu, Hanzhong Zhong, Xiang Li
The shape control of deformable linear objects (DLOs) is challenging, since it is difficult to obtain the deformation models. Previous studies often approximate the models in purel…
Adaptive Control for Robotic Manipulation of Deformable Linear Objects with Offline and Online Learning of Unknown Models
Mingrui Yu, Hanzhong Zhong, Fangxun Zhong +1
The deformable linear objects (DLOs) are common in both industrial and domestic applications, such as wires, cables, ropes. Because of its highly deformable nature, it is difficult…