Publications

Journal Paper

An inversion-based learning approach for improving impromptu trajectory tracking of robots with non-minimum phase dynamics
S. Zhou, M. K. Helwa, and A. P. Schoellig
in the IEEE Robotics and Automation Letters (RA-L) 2018
arXiv preprint: 1709.04407 | final version

Conference Paper

Design of deep neural networks as add-on blocks for improving impromptu trajectory tracking
S. Zhou, M. K. Helwa, and A. P. Schoellig
presented at the IEEE Conference on Decision and Control (CDC) 2017
arXiv preprint: 1705.10932 | final version

Extended Abstracts

Deep neural networks as add-on modules for high-accuracy impromptu trajectory tracking
S. Zhou, M. K. Helwa, and A. P. Schoellig
presented at the Conference on Robot Learning (CoRL) 2017
abstract | supplementary materials | short talk

A comparison of probabilistic population code and sampling-based code in neural state estimations
S. Zhou
presented at the Conference on Cognitive Computational Neuroscience (CCN) 2017
abstract

Under Review

Deep neural networks as add-on modules for enhancing robot performance in impromptu trajectory tracking
S. Zhou, M. K. Helwa, and A. P. Schoellig
submitted to the International Journal of Robotics Research (IJRR) September 2018