The Cognitive Optimization and Relational (CORE) Robotics laboratory develops advanced algorithmic techniques to enable robots to collaborate with human teammates. The lab’s work is founded upon the vision for moving beyond thinking of robots as tools that must be manually controlled towards a paradigm in which robots can learn through interaction and experience how to be effective peers for human professionals in healthcare, manufacturing, and search & rescue. Algorithmic techniques include deep reinforcement learning, mathematical programming, and distributed control theory. Additionally, we conduct human subject experiments to understand how to design effective algorithms and to evaluate the contribution of our computational techniques.
- April 25, 2018 – Mariah Schrum’s extended abstract “Improving Clinical Care of Pediatric Cerebral Palsy…” was accepted to ICRA’s 2019 Human Movement Science for Physical Human-Robot Collaboration Workshop. Congratulations!
- December 18, 2018 – Andrew Silva’s paper “Encoding Domain Knowledge …” was accepted to NAML’19 for an oral presentation. Rohan Paleja’s paper “Heterogeneous Learning…” will be included in the HRI Pioneers 2019 Workshop in Daegu, Korea where he’ll be giving his presentation at the workshop and a poster presentation at the main conference.
- November 16, 2018 – Congratulations to Matthew Gombolay who received the R&D 100 award for his recent work on learning from demonstration for optimization. Read the paper here.