Iterative trajectory optimization techniques for non-linear dynamical systems are among the
most powerful and sample-efficient methods of model-based reinforcement learning and …

Trajectory optimization and model predictive control are essential techniques underpinning
advanced robotic applications, ranging from autonomous driving to full-body humanoid …

Optimal control under uncertainty is a prevailing challenge for many reasons. One of the
critical difficulties lies in producing tractable solutions for the underlying stochastic …

Goals for planning problems are typically conceived of as subsets of the state space.
However, for many practical planning problems in robotics, we expect the robot to predict …

Machinelearningand its variousapplications have driven innovation in robotics, synthetic
perception, and data analytics. The last decade especially has experienced an explosion in …