5 citations · 13 across the 7 of their papers we have counts for
7 papers
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,…
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…
Robust Robot-assisted Tele-grasping Through Intent-Uncertainty-Aware Planning
Michael Bowman, Songpo Li, Xiaoli Zhang
In teleoperation, research has mainly focused on target approaching, where we deal with the more challenging object manipulation task by advancing the shared control technique. App…
A General Arbitration Model for Robust Human-Robot Shared Control with Multi-Source Uncertainty Modeling
Songpo Li, Michael Bowman, Xiaoli Zhang
Shared control in teleoperation leverages both human and robot's strengths and has demonstrated great advantages of reducing the difficulties in teleoperating a robot and increasin…
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…