8 citations · 20 across the 10 of their papers we have counts for
10 papers
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…
A Multi-Agent Approach for Adaptive Finger Cooperation in Learning-based In-Hand Manipulation
Lingfeng Tao, Jiucai Zhang, Michael Bowman +1
In-hand manipulation is challenging for a multi-finger robotic hand due to its high degrees of freedom and the complex interaction with the object. To enable in-hand manipulation,…
Physics-Guided Hierarchical Reward Mechanism for Learning-Based Robotic Grasping
Yunsik Jung, Lingfeng Tao, Michael Bowman +2
Learning-based grasping can afford real-time grasp motion planning of multi-fingered robotics hands thanks to its high computational efficiency. However, learning-based methods are…
Multi-Phase Multi-Objective Dexterous Manipulation with Adaptive Hierarchical Curriculum
Lingfeng Tao, Jiucai Zhang, Xiaoli Zhang
Dexterous manipulation tasks usually have multiple objectives, and the priorities of these objectives may vary at different phases of a manipulation task. Varying priority makes a…
Forming Real-World Human-Robot Cooperation for Tasks With General Goal
Lingfeng Tao, Michael Bowman, Jiucai Zhang +1
In human-robot cooperation, the robot cooperates with humans to accomplish the task together. Existing approaches assume the human has a specific goal during the cooperation, and t…
Learn and Transfer Knowledge of Preferred Assistance Strategies in Semi-autonomous Telemanipulation
Lingfeng Tao, Michael Bowman, Xu Zhou +2
Enabling robots to provide effective assistance yet still accommodating the operator's commands for telemanipulation of an object is very challenging because robot's assistive acti…
Learn Task First or Learn Human Partner First: A Hierarchical Task Decomposition Method for Human-Robot Cooperation
Lingfeng Tao, Michael Bowman, Jiucai Zhang +1
Applying Deep Reinforcement Learning (DRL) to Human-Robot Cooperation (HRC) in dynamic control problems is promising yet challenging as the robot needs to learn the dynamics of the…