Reinforcement learning (RL) has shown promising results for real-time co...
Animals have evolved various agile locomotion strategies, such as sprint...
The ability to effectively reuse prior knowledge is a key requirement wh...
We investigate the use of prior knowledge of human and animal movement t...
We study the problem of robotic stacking with objects of complex geometr...
Robot manipulation requires a complex set of skills that need to be care...
Modern Reinforcement Learning (RL) algorithms promise to solve difficult...
Solutions to most complex tasks can be decomposed into simpler, intermed...
Many real-world problems require trading off multiple competing objectiv...
Off-policy reinforcement learning algorithms promise to be applicable in...
Many real-world control problems involve both discrete decision variable...
Humans are masters at quickly learning many complex tasks, relying on an...
The successful application of flexible, general learning algorithms -- s...
We present a method for fast training of vision based control policies o...
The naive application of Reinforcement Learning algorithms to continuous...
We propose Scheduled Auxiliary Control (SAC-X), a new learning paradigm ...
We introduce the Control Toolbox (CT), an open-source C++ library for
ef...
In this work we present a whole-body Nonlinear Model Predictive Control
...
This paper introduces a family of iterative algorithms for unconstrained...