Anushri Dixit

Bio: Anushri Dixit is a Ph.D. candidate at California Institute of Technology in Control and Dynamical Systems and is advised by Prof. Joel Burdick. Her research focuses on motion planning and control of robots in extreme terrain while accounting for uncertainty in a principled manner. Her work on risk-aware methodologies for planning has been deployed on various robotic platforms as a part of the Team CoSTAR’s effort in the DARPA Subterranean Challenge. She has received the DE Shaw Zenith Fellowship and was selected as a rising star at the Southern California Robotics Symposium. Prior to her Ph.D., she earned her B.S. in Electrical Engineering from Georgia Institute of Technology in 2017.

Talk Title: Towards Risk-Aware Robotic Autonomy in Extreme Environments: Robustness to Distribution Shifts in Control and Planning

Talk Abstract: Robots provide the crucial ability to replace humans in environments that are inaccessible due to environmental hazards such as search and rescue operations. They need to be able to reason about the risk in an environment that is perceptually degraded and complete tasks while maintaining safety. Providing safety and performance guarantees for motion planning and control algorithms is a well-studied problem for robotic systems with well-known dynamics that operate in structured environments. However, when robots operate in a real-world setting where the environment is dynamic and unstructured, common distributional assumptions used to develop the planning algorithms are no longer valid and consequently, the safety guarantees no longer hold. In this talk, I will focus on risk-aware methodologies for robotic autonomy in unstructured environments. I will provide data-driven motion planning techniques to account for uncertainty in dynamic environments and sample-based risk bounds to provide high-confidence verification statements for robotic systems. The goal of the talk is to understand how robots interpret the ambiguity in their environment, and the ways to generate policies that better account for this uncertainty.