Title: Explainable AI for Achieving Shared Expectations During Human-Robot Collaboration

Presenter: Assistant Professor, Computer Science, University of Colorado Boulder

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Abstract: Robots capable of collaborating with humans will bring transformative changes to the way we live and work. In domains ranging from healthcare to domestic tasks to manufacturing, particularly under conditions where modern automation is ineffective or inapplicable, robots have the potential to increase humans' efficiency, capability, and safety. Despite this, the deployment of collaborative robots into human-dominated environments remains largely infeasible due to challenges involved in ensuring that our autonomous teammates are helpful and safe. In this talk I will present an overview of my group's work toward overcoming these challenges, realizing adaptive, communicative robot collaborators that both learn and dynamically assist in the completion of complex tasks through the application of novel learning and control algorithms. In particular, I will be focusing on the importance of explainability and the human-interpretable models that underpin these methods, discussing new research targeting the establishment of shared expectations between humans and robots, helping to ensure safe and efficient operation in the domains of learning from demonstration and collaborative task execution.

Bio: Dr. Bradley Hayes is an Assistant Professor of Computer Science at the University of Colorado Boulder, where he directs the Collaborative AI and Robotics (CAIRO) Lab. He received his Ph.D. in Computer Science from Yale University advised by Brian Scassellati, and performed research as a postdoctoral associate at the MIT Interactive Robotics Group working with Julie Shah. His work focuses on developing novel explainable AI and interpretable machine learning techniques for safe task and motion planning with a focus on human-robot collaboration, enabling the creation of trustworthy autonomous teammates that efficiently learn from, capably work with, and demonstrably improve the performance of human partners.