Alexander Tyshka — accepted AI-conference papers

2023

Interactive Task Learning for Social Robots: A Pilot Study

Alexander Tyshka, Wing-Yue Geoffrey Louie

IROS 2023poster

For socially assistive robots to achieve widespread adoption, the ability to learn new tasks in the wild is critical. Learning from Demonstration (LfD) approaches are a popular method for learning in the wild, but current methods require significant amounts of data and can be difficult to interpret.…

Cited by 0 Source ↗Scholar ↗

2022

Learning Turn-Taking Behavior from Human Demonstrations for Social Human-Robot Interactions

Pourya Shahverdi, Alexander Tyshka, Madeline Trombly, Wing-Yue Geoffrey Louie

IROS 2022poster

Turn-taking is a fundamental behavior during human interactions and robots must be capable of turn-taking to interact with humans. Current state-of-the-art approaches in turn-taking focus on developing general models to predict the end of turn (EoT) across all contexts. This demands an all-inclusive…

Cited by 2 Source ↗Scholar ↗