16 citations · 30 across the 10 of their papers we have counts for
10 papers
Towards Precise Model-free Robotic Grasping with Sim-to-Real Transfer Learning
Lei Zhang, Kaixin Bai, Zhaopeng Chen +2
Precise robotic grasping of several novel objects is a huge challenge in manufacturing, automation, and logistics. Most of the current methods for model-free grasping are disadvant…
Learning 6-DoF Task-oriented Grasp Detection via Implicit Estimation and Visual Affordance
Wenkai Chen, Hongzhuo Liang, Zhaopeng Chen +2
Currently, task-oriented grasp detection approaches are mostly based on pixel-level affordance detection and semantic segmentation. These pixel-level approaches heavily rely on the…
Sim-to-Real Transfer of Robotic Assembly with Visual Inputs Using CycleGAN and Force Control
Chengjie Yuan, Yunlei Shi, Qian Feng +4
Recently, deep reinforcement learning (RL) has shown some impressive successes in robotic manipulation applications. However, training robots in the real world is nontrivial owing…
Maximizing the Use of Environmental Constraints: A Pushing-Based Hybrid Position/Force Assembly Skill for Contact-Rich Tasks
Yunlei Shi, Zhaopeng Chen, Lin Cong +6
The need for contact-rich tasks is rapidly growing in modern manufacturing settings. However, few traditional robotic assembly skills consider environmental constraints during task…
Impedance Adaptation by Reinforcement Learning with Contact Dynamic Movement Primitives
Chunyang Chang, Kevin Haninger, Yunlei Shi +3
Dynamic movement primitives (DMPs) allow complex position trajectories to be efficiently demonstrated to a robot. In contact-rich tasks, where position trajectories alone may not b…
Learning compliant grasping and manipulation by teleoperation with adaptive force control
Chao Zeng, Shuang Li, Yiming Jiang +4
In this work, we focus on improving the robot's dexterous capability by exploiting visual sensing and adaptive force control. TeachNet, a vision-based teleoperation learning framew…
TransSC: Transformer-based Shape Completion for Grasp Evaluation
Wenkai Chen, Hongzhuo Liang, Zhaopeng Chen +2
Currently, robotic grasping methods based on sparse partial point clouds have attained a great grasping performance on various objects while they often generate wrong grasping cand…
Combining Learning from Demonstration with Learning by Exploration to Facilitate Contact-Rich Tasks
Yunlei Shi, Zhaopeng Chen, Yansong Wu +5
Collaborative robots are expected to be able to work alongside humans and in some cases directly replace existing human workers, thus effectively responding to rapid assembly line…
Proactive Action Visual Residual Reinforcement Learning for Contact-Rich Tasks Using a Torque-Controlled Robot
Yunlei Shi, Zhaopeng Chen, Hongxu Liu +5
Contact-rich manipulation tasks are commonly found in modern manufacturing settings. However, manually designing a robot controller is considered hard for traditional control metho…
Center-of-Mass-based Robust Grasp Planning for Unknown Objects Using Tactile-Visual Sensors
Qian Feng, Zhaopeng Chen, Jun Deng +3
An unstable grasp pose can lead to slip, thus an unstable grasp pose can be predicted by slip detection. A regrasp is required afterwards to correct the grasp pose in order to fini…