Abstract
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
