📣 Help Shape the Future of UKRI's Gateway to Research (GtR)

We're improving UKRI's Gateway to Research and are seeking your input! If you would be interested in being interviewed about the improvements we're making and to have your say about how we can make GtR more user-friendly, impactful, and effective for the Research and Innovation community, please email gateway@ukri.org.

Imitation learning for sim-to-real adaptation of robotic cutting policies based on residual Gaussian process disturbance force model (2024)

First Author: Hathaway J
Attributed to:  National Centre for Nuclear Robotics (NCNR) funded by ISCF

Abstract

No abstract provided

Bibliographic Information

Digital Object Identifier: http://dx.doi.org/10.1109/iros58592.2024.10802660

Publication URI: http://dx.doi.org/10.1109/iros58592.2024.10802660

Type: Conference/Paper/Proceeding/Abstract