In the past couple of years, the interest of the fluid mechanics community for deep
reinforcement learning techniques has increased at fast pace, leading to a growing …

Most policy search (PS) algorithms require thousands of training episodes to find an
effective policy, which is often infeasible with a physical robot. This survey article focuses on …

The goal of multi-task learning is to enable more efficient learning than single task learning
by sharing model structures for a diverse set of tasks. A standard multi-task learning …

While deep learning and deep reinforcement learning (RL) systems have demonstrated
impressive results in domains such as image classification, game playing, and robotic …

We propose a novel, object-agnostic method for learning a universal policy for dexterous
object grasping from realistic point cloud observations and proprioceptive information under …

We introduce a conceptually simple and scalable framework for continual learning domains
where tasks are learned sequentially. Our method is constant in the number of parameters …

We present relay policy learning, a method for imitation and reinforcement learning that can
solve multi-stage, long-horizon robotic tasks. This general and universally-applicable, two …

General-purpose robotic systems must master a large repertoire of diverse skills to be useful
in a range of daily tasks. While reinforcement learning provides a powerful framework for …

Abstract We propose Scheduled Auxiliary Control (SAC-X), a new learning paradigm in the
context of Reinforcement Learning (RL). SAC-X enables learning of complex behaviors-from …

Abstract Knowledge distillation introduced in the deep learning context is a method to
transfer knowledge from one architecture to another. In particular, when the architectures are …