2 code implementations • 13 Apr 2022 • Oier Mees, Lukas Hermann, Wolfram Burgard
We have open-sourced our implementation to facilitate future research in learning to perform many complex manipulation skills in a row specified with natural language.
1 code implementation • 1 Mar 2022 • Jessica Borja-Diaz, Oier Mees, Gabriel Kalweit, Lukas Hermann, Joschka Boedecker, Wolfram Burgard
Robots operating in human-centered environments should have the ability to understand how objects function: what can be done with each object, where this interaction may occur, and how the object is used to achieve a goal.
1 code implementation • 6 Dec 2021 • Oier Mees, Lukas Hermann, Erick Rosete-Beas, Wolfram Burgard
We show that a baseline model based on multi-context imitation learning performs poorly on CALVIN, suggesting that there is significant room for developing innovative agents that learn to relate human language to their world models with this benchmark.
no code implementations • 29 Apr 2021 • Artemij Amiranashvili, Max Argus, Lukas Hermann, Wolfram Burgard, Thomas Brox
Visual domain randomization in simulated environments is a widely used method to transfer policies trained in simulation to real robots.
no code implementations • 2 Aug 2020 • Iman Nematollahi, Oier Mees, Lukas Hermann, Wolfram Burgard
A key challenge for an agent learning to interact with the world is to reason about physical properties of objects and to foresee their dynamics under the effect of applied forces.
no code implementations • 1 Jul 2020 • Max Argus, Lukas Hermann, Jon Long, Thomas Brox
One-shot imitation is the vision of robot programming from a single demonstration, rather than by tedious construction of computer code.
1 code implementation • 17 Oct 2019 • Lukas Hermann, Max Argus, Andreas Eitel, Artemij Amiranashvili, Wolfram Burgard, Thomas Brox
We propose Adaptive Curriculum Generation from Demonstrations (ACGD) for reinforcement learning in the presence of sparse rewards.