Human-Robot Alignment through Interactivity and Interpretability: Don't Assume a ``Spherical Human''
Human-Robot Alignment through Interactivity and Interpretability: Don't Assume a ``Spherical Human''
Matthew Gombolay
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence
Early Career. Pages 8523-8528.
https://doi.org/10.24963/ijcai.2024/976
Interactive and interpretable robot learning can help to democratize robots, placing the power of assistive robotic systems in the hands of end-users. While machine learning-based approaches to robotics have achieved impressive results, robot learning is still a feat of costly engineering performed in controlled settings and relying upon impractical assumptions about humans. To achieve a vision in which robots can be integrated sustainably into our daily lives for robotic assistance, researchers must take a human-centered approach and develop novel approaches for human-robot alignment of robot values and behaviors. This paper amalgamates recent human factors insights and computational techniques that can support human-robot alignment through interactive and interpretable robot learning and teaming.
Keywords:
Robotics: ROB: Human robot interaction
Machine Learning: ML: Explainable/Interpretable machine learning
Robotics: ROB: Learning in robotics