Rachel Hegeman — accepted AI-conference papers

2019

Autonomous Data-Driven Manipulation of Unknown Anisotropic Deformable Tissues Using Unmodelled Continuum Manipulators

Farshid Alambeigi, Zerui Wang, Rachel Hegeman, Yun-Hui Liu, Mehran Armand

RA-L 2019

We present an autonomous manipulation approach for tissues with anisotropic deformation behavior using a continuum manipulator. The key feature of our vision-based study is an online learning and estimation method, which makes its implementation independent of any prior knowledge about the deformati

Cited by 50 Source ↗Scholar ↗

2018

A Robust Data-Driven Approach for Online Learning and Manipulation of Unmodeled 3-D Heterogeneous Compliant Objects

Farshid Alambeigi, Zerui Wang, Rachel Hegeman, Yun-Hui Liu, Mehran Armand

RA-L 2018

We present a generic data-driven method to address the problem of manipulating a three-dimensional (3-D) compliant object (CO) with heterogeneous physical properties in the presence of unknown disturbances. In this study, we do not assume a prior knowledge about the deformation behavior of the CO an

Cited by 50 Source ↗Scholar ↗