1 citations · 1 across the 3 of their papers we have counts for
4 papers · 1 filter
Real-time Dexterous Telemanipulation with an End-Effect-Oriented Learning-based Approach
Haoyang Wang, He Bai, Xiaoli Zhang +3
Dexterous telemanipulation is crucial in advancing human-robot systems, especially in tasks requiring precise and safe manipulation. However, it faces significant challenges due to…
Curriculum-based Sensing Reduction in Simulation to Real-World Transfer for In-hand Manipulation
Lingfeng Tao, Jiucai Zhang, Qiaojie Zheng +1
Simulation to Real-World Transfer allows affordable and fast training of learning-based robots for manipulation tasks using Deep Reinforcement Learning methods. Currently, Sim2Real…
Stable In-hand Manipulation with Finger Specific Multi-agent Shadow Reward
Lingfeng Tao, Jiucai Zhang, Xiaoli Zhang
Deep Reinforcement Learning has shown its capability to solve the high degrees of freedom in control and the complex interaction with the object in the multi-finger dexterous in-ha…
Transferability-based Chain Motion Mapping from Humans to Humanoids for Teleoperation
Matthew Stanley, Yunsik Jung, Michael Bowman +2
Although data-driven motion mapping methods are promising to allow intuitive robot control and teleoperation that generate human-like robot movement, they normally require tedious…