Roberto Leo Medrano — accepted AI-conference papers

2025

Unsupervised Domain Adaptation for Gait State Estimation

Roberto Leo Medrano, Gray Cortright Thomas, Elliott J. Rouse

ICRA 2025

Exoskeleton controllers have recently employed machine learning (ML) techniques to provide appropriate assistance throughout the terrains of the real world. One successful approach has been to learn a mapping between an exoskeleton wearer's kinematic measurements and a gait state vector that encodes

Cited by 0 Source ↗Scholar ↗

2022

Analysis of the Bayesian Gait-State Estimation Problem for Lower-Limb Wearable Robot Sensor Configurations

Roberto Leo Medrano, Gray Cortright Thomas, Elliott J. Rouse, Robert D. Gregg

RA-L 2022

Many exoskeletons today are primarily tested in controlled, steady-state laboratory conditions that are unrealistic representations of their real-world usage in which walking conditions (<i>e</i>.<i>g</i>., speed, slope, and stride length) change constantly. One potential solution is to detect these

Cited by 7 Source ↗Scholar ↗