Motion planning under differential constraints is one of the canonical problems in robotics.
State-of-the-art methods evolve around kinodynamic variants of popular sampling-based …

This paper describes a probabilistic method for realtime decision making and motion
planning for autonomous vehicles. Our approach relies on the fact that driving on road …

Several sampling-based algorithms have been recently proposed that ensure asymptotic
optimality. The convergence of these algorithms can be improved if sampling is guided …

Motion planning for autonomous robots in dynamic environments poses numerous
challenges due to uncertainties in the robot's dynamics and interaction with other agents …

In this paper, we derive the path integral control algorithm to solve a discrete-time stochastic
Linear Quadratic Regulator (LQR) problem and carry out its sample complexity analysis …

This paper addresses planning and control of robot motion under uncertainty that is
formulated as a continuous-time, continuous-space stochastic optimal control problem, by …

We propose a new method for the numerical solution of the forward-backward stochastic
differential equations (FBSDE) appearing in the Feynman-Kac representation of the value …

This work presents an efficient method to solve a class of continuous-time, continuous-space
stochastic optimal control problems of robot motion in a cluttered environment. The method …

Model predictive control (MPC) is garnering attention in various fields owing to its attractive
characteristics such as its direct use in non-linear dynamic models, and application in …

We propose a numerical method for the computation of the forward-backward stochastic
differential equations (FBSDE) appearing in the Feynman-Kac representation of the value …