Lockheed Martin Robotics Seminar
Controlling Agile Robots with Formal Safety Guarantees
Department of Mechanical and Aerospace Engineering
The goal of my research is to develop algorithmic techniques for controlling highly agile robotic systems such as unmanned aerial vehicles while guaranteeing that they operate in a safe and reliable manner.
In this talk, I will describe our work on convex optimization-based algorithms for the synthesis of feedback controllers that come with associated formal guarantees on the stability of the robot and show how these controllers and certificates of stability can be used for robust planning in environments previously unseen by the system. In order to make these results possible, we leverage powerful computational tools such as sum of squares (SOS) programming and semidefinite programming, along with approaches from nonlinear control theory. I will describe this work in the context of high-speed unmanned aerial vehicle flight through cluttered environments. The resulting hardware demonstrations on a small fixed-wing airplane constitute one of the first examples of guaranteed safe and robust control for robotic systems with complex nonlinear dynamics that need to plan in realtime in environments with complex geometric constraints.
I will also present our recent work on ensuring safety in applications where robots and humans interact. In particular, I will describe our work on Risk-sensitive Inverse Reinforcement Learning (IRL) for modeling, inferring, and predicting the behavior of humans operating in a robot’s environment. In contrast to prior work on IRL that assumes that humans are risk neutral, our approach is able to infer humans’ risk preferences from observations of their actions. This allows us to better predict and imitate the human decision making process. We believe that such an approach is an important step towards being able to ensure safety in domains where robots and humans interact.
Dr. Majumdar's research focuses on the control of highly agile robotic systems such as unmanned aerial vehicles with formal guarantees on their safety and performance. Majumdar received a Ph.D. in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology in 2016, and a B.S.E. in Mechanical Engineering and Mathematics from the University of Pennsylvania in 2011. Subsequently, he was a postdoctoral scholar at Stanford University from 2016 to 2017 at the Autonomous Systems Lab in the Aeronautics and Astronautics department. His research has been recognized with the Best Conference Paper Award at the International Conference on Robotics and Automation (ICRA) 2013, and the Siebel Foundation Scholarship.