Towards Better Robot Learners: Leveraging Implicit and Explicit Human Feedback Together in Human Robot Interactions
Abstract
My work aims to enable robots to better learn from human feedback in human-robot interactions. The way in which people want to collaborate with a robot can vary person-to-person, interaction-to-interaction, or even within an interaction with a given person. Thus, robots need to be able to adapt their behavior during interactions. Robots typically learn from humans via explicit feedback, such as evaluative feedback, preferences, or demonstrations. We know that humans also provide additional information implicitly through non-verbal behavior that gives clues about their internal states during interactions. My work investigates how we can incorporate both kinds of feedback into robot learning paradigms.
Cite
Text
Candon. "Towards Better Robot Learners: Leveraging Implicit and Explicit Human Feedback Together in Human Robot Interactions." AAAI Conference on Artificial Intelligence, 2025. doi:10.1609/AAAI.V39I28.35202Markdown
[Candon. "Towards Better Robot Learners: Leveraging Implicit and Explicit Human Feedback Together in Human Robot Interactions." AAAI Conference on Artificial Intelligence, 2025.](https://mlanthology.org/aaai/2025/candon2025aaai-better/) doi:10.1609/AAAI.V39I28.35202BibTeX
@inproceedings{candon2025aaai-better,
title = {{Towards Better Robot Learners: Leveraging Implicit and Explicit Human Feedback Together in Human Robot Interactions}},
author = {Candon, Kate},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2025},
pages = {29249-29250},
doi = {10.1609/AAAI.V39I28.35202},
url = {https://mlanthology.org/aaai/2025/candon2025aaai-better/}
}