Kyle T. Yoshida — accepted AI-conference papers

2024

Modeling and Experimental Verification of a Continuous Curvature-Based Soft Growing Manipulator

Justin Allen, Ryan Dorosh, Christopher Ninatanta, Andrew Allen, Linlin Shui, Kyle T. Yoshida +2

RA-L 2024

Soft robots show significant potential for use in search and rescue, human-robot interaction, and other emerging fields due to their ability to easily conform, deform, and interact with their environment. However, precise control of these soft robots is still being explored. In this letter, we inves

Cited by 8 Source ↗Scholar ↗

2022

Deep Learning Classification of Touch Gestures Using Distributed Normal and Shear Force

Hojung Choi, Dane Brouwer, Michael A. Lin, Kyle T. Yoshida, Carine Rognon, Benjamin Stephens-Fripp +2

IROS 2022poster

When humans socially interact with another agent (e.g., human, pet, or robot) through touch, they do so by applying varying amounts of force with different directions, locations, contact areas, and durations. While previous work on touch gesture recognition has focused on the spatio-temporal distrib…

Cited by 13 Source ↗Scholar ↗