Current ResearchMy research lies at the intersection of Machine Learning, Control Theory, and Robotics. I am particularly interested in
Other than robotics, I am also interested in the HamiltonJacobi Reachability theory and its application to multiagent autonomous systems. Machine Learning and Control TheoryRealworld autonomous systems are becoming increasingly complex and commonly act in poorly understood environments where it is extremely challenging to model their true dynamics. While machine learning and AI have the potential to transform autonomous systems, this transformation requires systems which are robust, resilient and safe. Repeated experiments to gain realistic data is typically not a possibility, and models and analysis must be used in a principled way to enable this transformation. Motivated by this, my research focuses on learning dataefficient models from the learning perspective, and on designing efficient control/analysis schemes for these datadriven models from the control theory perspective. Apart from control, an important lingering question for the autonomous systems is that of their safety and performance analysis. In order to verify a system, the control community analyzes a validated system model, using HamiltonJacobi reachability for instance, and provide formal performance and safety guarantees on that system model. In theory, the same tools can be used to verify a datadriven model, except that one first needs to validate that model to make sure that the analysis conducted on it holds for the actual system. We are currently developing some sampling and machine learning based methods to validate a datadriven system model. HamiltonJacobi ReachabilityHamiltonJacobi (HJ) reachability is a useful tool for guaranteeing goal satisfaction and safety, and has been used in various safetycritical scenarios including aircraft autolanding, automated aerial refueling, largescale multiplevehicle path planning, etc. In reachability theory, one is interested in computing the Backward Reachable Set (BRS), the set of states such that the trajectories that start from this set can reach some given target set. If the target set consists of those states that are known to be unsafe, then the BRS contains states which are potentially unsafe and should therefore be avoided. Traditionally, reachable set computations involve solving an HJ partial differential equation on a grid representing a discretization of the state space, resulting in an exponential scaling of computational complexity with respect to system dimensionality; this is often referred to as the “curse of dimensionality.” My research focuses on how we can overcome this curse by exploiting system structures.
