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Research Overview

Intrinsically Motivated Learning

Regularity as Intrinsic Reward for Free Play

SENSEI: Semantic Exploration Guided by Foundation Models to Learn Versatile World Models

Curious Exploration via Structured World Models Yields Zero-Shot Object Manipulation

Learning with Muscles

Natural and Robust Walking from Generic Rewards

The effect of muscles in Learning Behavior

Scaling RL to Large Musculoskeletal Systems

Reinforcement Learning for Diverse Solutions

Offline Diversity Under Imitation Constraints

Learning Diverse Skills for Local Navigation

Learning Agile Skills via Adversarial Imitation of Rough Partial Demonstrations

Reinforcement Learning and Control

Model-based Reinforcement Learning and Planning

Causal Reasoning in RL

Intrinsically Motivated Hierarchical Learner

Regularity as Intrinsic Reward for Free Play

Curious Exploration via Structured World Models Yields Zero-Shot Object Manipulation

Natural and Robust Walking from Generic Rewards

Goal-conditioned Offline Planning

Offline Diversity Under Imitation Constraints

Learning Diverse Skills for Local Navigation

Learning Agile Skills via Adversarial Imitation of Rough Partial Demonstrations

Deep Learning

Combinatorial Optimization as a Layer / Blackbox Differentiation

Symbolic Regression and Equation Learning

Representation Learning

Learning with 3D rotations: A hitchhiker’s guide to SO(3)

Haptic Sensing

Super-resolution Sensing for Haptics

Insight: a Haptic Sensor Powered by Vision and Machine Learning

Minsight: Learning-based tactile sensing for robotics

Previous Research Projects

Learning to Control Emulated Muscles in Real Robots: A Software Test Bed for Bio-Inspired Actuators in Hardware

Combinatorial Optimization as a Layer / Blackbox Differentiation

Causal Action Influence Aware Counterfactual Data Augmentation

Dual-Force: Enhanced Offline Diversity Maximization under Imitation Constraints

Versatile Skill Control via Self-supervised Adversarial Imitation of Unlabeled Mixed Motions

On Imitation in Mean-field Games

Online Learning under Adversarial Nonlinear Constraints

Autonomous Learning

ML for Science

In various collaborations, we use machine learning methods in other scientific Domains to advance our understanding and gain new insights. One of our own machine learning method, called the equation learner, is particularly suitable for applications in the sciences, as it attempts to find very concise analytical equations for data. 

Projects

In this project, we look at the prediction of brain activity, measured by activation maps in fMRI, that occur while performing a task from the activation that occurs during resting. The latter means the patients are asked to rest in... Read more

While simple classical fluids are reasonably well understood, theories about the emerging physical behaviour of fluid particles that interact through an anisotropic potential are still quite limited. We are using machine learning... Read more

We are interested in deploying machine learning methods to improve the understanding of quantum systems. Given a physical system in which a subsystem is embedded in a larger system, i.e. a bath, we used neural networks to learn the... Read more

Symbolic Regression and Equation Learning

When analyzing data in the context of natural sciences, the prime aim is often to gain understanding by distilling compact and interpretable analytic equations. Instead, contemporary supervised machine learning methods mostly... Read more