JST-CREST / IEEE-RAS Spring School on "Social and Artificial Intelligence for User-Friendly Robots"

17-24 March 2019, Shonan Village, Japan

Invited Speakers

  • Bradley Hayes, University of Colorado Boulder, US
  • Bradley Hayes 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.