Title: Explainable AI for Human-Robot Collaboration
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 can increase humans' efficiency, capability, and safety.
Despite this, the deployment of collaborative robots into human-dominated
environments remains largely infeasible due to the myriad challenges involved in
ensuring our autonomous teammates are helpful and safe. In this talk I will present
an overview of my work toward overcoming these challenges, realizing flexible,
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, helping to ensure
safe and efficient operation in 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 Lab. He
received his Ph.D. in Computer Science from Yale University in 2015 and performed
research as a postdoctoral associate in MIT's Interactive Robotics Group. His work
focuses on developing novel explainable AI and interpretable machine learning
techniques for safe task and motion planning within human-robot collaboration,
enabling autonomous teammates to efficiently learn from, capably work with, and
improve the performance of humans.