1 citations · 1 across the 2 of their papers we have counts for
4 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…
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…
Physics-informed machine learning for composition-process-property alloy design: shape memory alloy demonstration
Sen Liu, Branden B. Kappes, Behnam Amin-ahmadi +3
Machine learning (ML) is shown to predict new alloys and their performances in a high dimensional, multiple-target-property design space that considers chemistry, multi-step proces…