  Air hockey is a highly reactive game which requires the player to quickly
reason over stochastic puck and contact dynamics. We implement a hierarchical
framework which combines stochastic optimal control for planning shooting
angles and sampling-based model-predictive control for continuously generating
constrained mallet trajectories. Our agent was deployed and evaluated in
simulation and on a physical setup as part of the Robot Air-Hockey challenge
competition at NeurIPS 2023.
