Sajay Arthanat — accepted AI-conference papers

2021

Learning to Optimize Control Policies and Evaluate Reproduction Performance from Human Demonstrations

Paul Gesel, Dain LaRoche, Sajay Arthanat, Momotaz Begum

IROS 2021poster

We are interested in learning from demonstration (LfD) that can both learn and execute a trajectory and evaluate the quality of a previously unseen trajectory in the domain of assistive robotics. To this end, we propose a novel continuous inverse optimal control (IOC) formulation that simultaneously…

Cited by 1 Source ↗Scholar ↗

2020

Learning Optimized Human Motion via Phase Space Analysis

Paul Gesel, Francesco Mikulis-Borsoi, Dain LaRoche, Sajay Arthanat, Momotaz Begum

IROS 2020poster

This paper proposes a dynamic system based learning from demonstration approach to teach a robot activities of daily living. The approach takes inspiration from human movement literature to formulate trajectory learning as an optimal control problem. We assume a weighted combination of basis objecti…

Cited by 1 Source ↗Scholar ↗