Highlighted Research Directions
Short descriptions under blocks detail the research question(s) addressed by each block!
It is difficult and cumbersome to design decentralized multi-agent coordination algorithms that allow agents to perform tasks such as search-and-rescue. We instead use multi-agent reinforcement learning to allow decentralized robots to collectively share knowledge and develop collaboration plans.
Recent advances in machine learning have led to growing interest in xAI to enable humans to gain insight into the decision-making of machine learning models. However, 1) interpretable reinforcement learning remains an open challenge, and 2) the utility of interpretable models in human-machine teaming has not yet been characterized.
Learning from Demonstration
Having engineers program behavior for robots in each specific application across factories, hospitals, and households is intractable, can result in suboptimal robot policies, and may require access to domain information that is private. Thus, to truly achieve ubiquity of robotics, end-users must be able to interactively teach robots desired behaviors, thereby imparting their domain knowledge onto the robot.