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Walking the Boundary of Learning and Interaction
This seminar pairs a case study of AI diffusion with an organizational approach to technological changes in the workplace.
This seminar pairs a case study of AI diffusion with an organizational approach to technological changes in the workplace.
As AI moves beyond language into systems that can perceive, understand, and act in the physical world, a new frontier is emerging: world models—AI systems that build and maintain working representations of real environments to predict how they change in response to action.
As AI moves beyond language into systems that can perceive, understand, and act in the physical world, a new frontier is emerging: world models—AI systems that build and maintain working representations of real environments to predict how they change in response to action.
Save the Date. Artificial intelligence is transforming how researchers collect, analyze, and learn from data. As AI systems become increasingly integrated into scientific discovery, business decision-making, and policy analysis, they are reshaping both the questions researchers can ask and the methods they use to answer them.
Save the Date. Artificial intelligence is transforming how researchers collect, analyze, and learn from data. As AI systems become increasingly integrated into scientific discovery, business decision-making, and policy analysis, they are reshaping both the questions researchers can ask and the methods they use to answer them.
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.
