About This Episode
Today we’re joined by Julie Shah, a professor at the Massachusetts Institute of Technology (MIT). Julie’s work lies at the intersection of aeronautics, astronautics, and robotics, with a specific focus on collaborative and interactive robotics. In our conversation, we explore how robots would achieve the ability to predict what their human collaborators are thinking, what the process of building knowledge into these systems looks like, and her big picture idea of developing a field robot that doesn’t “require a human to be a robot” to work with it. We also discuss work Julie has done on cross-training between humans and robots with the focus on getting them to co-learn how to work together, as well as future projects that she’s excited about.
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- Paper: Human-Robot Cross-Training: Computational Formulation, Modeling and Evaluation of a Human Team Training Strategy
- Paper: Decision-Making for Bidirectional Communication in Sequential Human-Robot Collaborative Tasks
- Paper: Semi-Supervised Learning of Decision-Making Models for Human-Robot Collaboration
- Paper: Learning Models of Sequential Decision-Making with Partial Specification of Agent Behavior
- Book: What to Expect When You’re Expecting Robots