Early robots often occupied tightly controlled environments, e.g., factory floors, designed to segregate robots and humans for safety. In the near future, robots will "live" with humans, providing a variety of services at homes, in workplaces, or on the road. To become effective and trustworthy collaborators, robots must adapt to human preferences and more interestingly, adapt to changing human preferences, as humans adapt as well. I will discuss our recent work, covering mathematical models that leverage estimation of human intention for robot adaptation, planning algorithms that connect robot perception with decision making, and learning algorithms that enable robots to adapt to human preferences without a prior model. The discussion, I hope, will spur greater interest towards principled approaches that integrate perception, planning, and learning for fluid human-robot collaboration.
Speaker: David Hsu, National University of Singapore
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