Reinforcement Learning and Control

In several research projects, we investigate data-driven approaches to the control of robotic systems. By combining optimization and reinforcement learning, intrinsic motivation, and hierarchical planning, we are tackling various challenges arising in robotic systems. Please browse through the research topics in the menu on the side.

Projects

Model-based Reinforcement Learning and Planning

The goal of this project is to bring efficient learning-based control methods to real robots. A promising direction to learning from a few trials is to separate the task. First, train a model of the interactions with the environment and... Read more

Autonomous agents need large repertoires of skills to act reasonably on new tasks that they have not seen before. To learn such skills an agent can first learn a structured representation of the environment and then use it for the... Read more

Humans and other animals need to learn behavior from exploring their sensorimotor capabilities. In this project we aim to understand how robotic systems can generate structured behavior, without specifying any reward nor objective... Read more

Causal Reasoning in RL

Reinforcement learning is fundamentally a causal endeavor. The agent intervenes in the environment through actions and observes their effects; learning through credit assignment involves the counterfactual question whether another action... Read more

Intelligent systems today, including physical robots, are capable of amazing feats compared to their early predecessors. However, much of the planning and control relies on the accurate identification of the robotic system, often in... Read more

Intrinsically Motivated Hierarchical Learner

True autonomy in the real world is not bound to a single task under fixed environmental dynamics. In fact, tasks are hardly ever clearly specified, e.g. through well-shaped rewards, and it really is left to the agent to determine what... Read more