73 citations · 129 across the 17 of their papers we have counts for
12 papers · 1 filter
D3-ARM: High-Dynamic, Dexterous and Fully Decoupled Cable-driven Robotic Arm
Hong Luo, Jianle Xu, Shoujie Li +4
Cable transmission enables motors of robotic arm to operate lightweight and low-inertia joints remotely in various environments, but it also creates issues with motion coupling and…
Efficient Collision Detection Framework for Enhancing Collision-Free Robot Motion
Xiankun Zhu, Yucheng Xin, Shoujie Li +3
Fast and efficient collision detection is essential for motion generation in robotics. In this paper, we propose an efficient collision detection framework based on the Signed Dist…
MuxHand: A Cable-driven Dexterous Robotic Hand Using Time-division Multiplexing Motors
Jianle Xu, Shoujie Li, Hong Luo +4
The robotic dexterous hand is responsible for both grasping and dexterous manipulation. The number of motors directly influences both the dexterity and the cost of such systems. In…
Safe Expeditious Whole-Body Control of Mobile Manipulators for Collision Avoidance
Bingjie Chen, Yancong Wei, Rihao Liu +5
Whole-body reactive obstacle avoidance for mobile manipulators (MM) remains an open research problem. Control Barrier Functions (CBF), combined with Quadratic Programming (QP), hav…
CushionCatch: A Compliant Catching Mechanism for Mobile Manipulators via Combined Optimization and Learning
Bingjie Chen, Keyu Fan, Qi Yang +6
Catching flying objects with a cushioning process is a skill commonly performed by humans, yet it remains a significant challenge for robots. In this paper, we present a framework…
ViIK: Flow-based Vision Inverse Kinematics Solver with Fusing Collision Checking
Qinglong Meng, Chongkun Xia, Xueqian Wang
Inverse Kinematics (IK) is to find the robot's configurations that satisfy the target pose of the end effector. In motion planning, diverse configurations were required in case a f…
PPNet: A Two-Stage Neural Network for End-to-end Path Planning
Qinglong Meng, Chongkun Xia, Xueqian Wang +2
The classical path planners, such as sampling-based path planners, can provide probabilistic completeness guarantees in the sense that the probability that the planner fails to ret…
Learning Language-Conditioned Deformable Object Manipulation with Graph Dynamics
Yuhong Deng, Kai Mo, Chongkun Xia +1
Multi-task learning of deformable object manipulation is a challenging problem in robot manipulation. Most previous works address this problem in a goal-conditioned way and adapt g…
Deep Reinforcement Learning Based on Local GNN for Goal-conditioned Deformable Object Rearranging
Yuhong Deng, Chongkun Xia, Xueqian Wang +1
Object rearranging is one of the most common deformable manipulation tasks, where the robot needs to rearrange a deformable object into a goal configuration. Previous studies focus…
Graph-Transporter: A Graph-based Learning Method for Goal-Conditioned Deformable Object Rearranging Task
Yuhong Deng, Chongkun Xia, Xueqian Wang +1
Rearranging deformable objects is a long-standing challenge in robotic manipulation for the high dimensionality of configuration space and the complex dynamics of deformable object…
Foldsformer: Learning Sequential Multi-Step Cloth Manipulation With Space-Time Attention
Kai Mo, Chongkun Xia, Xueqian Wang +3
Sequential multi-step cloth manipulation is a challenging problem in robotic manipulation, requiring a robot to perceive the cloth state and plan a sequence of chained actions lead…
Visual-tactile Fusion for Transparent Object Grasping in Complex Backgrounds
Shoujie Li, Haixin Yu, Wenbo Ding +5
The accurate detection and grasping of transparent objects are challenging but of significance to robots. Here, a visual-tactile fusion framework for transparent object grasping un…