Robot planning is the process of selecting a sequence of actions that optimize for a task=
specific objective. For instance, the objective for a navigation task would be to find collision …

Human drivers have complex and individual behavior characteristics which describe how
they act in a specific situation. Accurate behavior models are essential for many applications …

Abstract Model-based reinforcement learning (MBRL) plays an important role in developing
control strategies for robotic systems. However, when dealing with complex platforms, it is …

In Receding Horizon Planning (RHP), it is critical that the motion being executed facilitates
the completion of the task, eg building momentum to overcome large obstacles. This …

This paper presents a method to enable a robot using stochastic Model Predictive Control
(MPC) to achieve high performance on a repetitive path-following task. In particular, we …

Planning multicontact motions in a receding horizon fashion requires a value function to
guide the planning with respect to the future, eg, building momentum to traverse large …

We consider imitation learning with access only to expert demonstrations, whose real-world
application is often limited by covariate shift due to compounding errors during execution …

Online imitation learning (IL) is an algorithmic framework that leverages interactions with
expert policies for efficient policy optimization. Here policies are optimized by performing …

Control algorithms such as stochastic model predictive control (SMPC) choose control inputs
that guide the robot towards its goal by minimising a cost function while limiting the risk of …

Human-like agents have the potential to drastically improve multiplayer, first-person shooter
(FPS) games. They can serve as engaging teammates, useful practice partners, and anti …