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Walking the Boundary of Learning and Interaction
The AI Index, currently in its ninth year, tracks, collates, distills, and visualizes data relating to artificial intelligence.
The AI Index, currently in its ninth year, tracks, collates, distills, and visualizes data relating to artificial intelligence.
Strategic stability exists when neither side thinks it can improve its strategic outcome by striking first.
Strategic stability exists when neither side thinks it can improve its strategic outcome by striking first.
The 2026 RAISE Health Symposium will convene leading voices in technology, medicine, and policy to explore what’s real, what’s next, and what’s needed to ensure AI advances responsibly and benefits all. Registration opens March 11.
The 2026 RAISE Health Symposium will convene leading voices in technology, medicine, and policy to explore what’s real, what’s next, and what’s needed to ensure AI advances responsibly and benefits all. Registration opens March 11.
There have been significant advances in the field of robot learning in the past decade. However, many challenges still remain when considering how robot learning can advance interactive agents such as robots that collaborate with humans. This includes autonomous vehicles that interact with human-driven vehicles or pedestrians, service robots collaborating with their users at homes over short or long periods of time, or assistive robots helping patients with disabilities. This introduces an opportunity for developing new robot learning algorithms that can help advance interactive autonomy.
In this talk, I will discuss a formalism for human-robot interaction built upon ideas from representation learning. Specifically, I will first discuss the notion of latent strategies — low dimensional representations sufficient for capturing non-stationary interactions. I will then talk about the challenges of learning such representations when interacting with humans, and how we can develop data-efficient techniques that enable actively learning computational models of human behavior from demonstrations, preferences, or physical corrections. Finally, I will introduce an intuitive controlling paradigm that enables seamless collaboration based on learned representations, and further discuss how that can be used for influencing humans.
