Research
Soft Robotics
Our team is currently using energy shaping control and optimal control method to control soft actuators in robots. We implement various control methods both on a simulated platform: CyberOctopus and a physical robot: BR2. These control strategies allow us to manipulate robots to performs various behaviors including reaching, grasping and crawling.
Reinforcement Learning using interacting particle systems
The objective here is to study the applicability of particle based methods to reinforcement learning problems.
Epidemiological Modeling
Nonlinear Filtering
In a recent work, we presented a dual model to transform the nonlinear filtering problem into a stochastic optimal control problem. The model has since been used for the purposes of defining observability and detectability for the nonlinear filtering problem, and for analysis of the filter stability.
Feedback Particle Filter
Feedback particle filter is a novel algorithm for nonlinear estimation that has been developed by our research group. It provides for a generalization of the Kalman filter to a general class of nonlinear non-Gaussian problems.