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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

Haptic Sensing

Super-resolution Sensing for Haptics

Insight: a Haptic Sensor Powered by Vision and Machine Learning

Minsight: Learning-based tactile sensing for robotics

ML for Science

Symbolic Regression and Equation Learning

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

Deep Learning

Deep learning is the tool for our research to obtain learned representations, fit functions such as policies or value functions and learn internal models. Along the way of using deep learning techniques for our core focus of autonomous learning we frequently need to develop new methods. Quite often, we stumble upon unsolved or puzzling problems in the techniques themselves. Some of which we are solving, see our projects below. In general, we are particularly interested in techniques for representation learning, internal model learning and continual learning.

Projects

Combinatorial Optimization as a Layer / Blackbox Differentiation

Deep learning has shown remarkable success in solving problems from high-dimensional raw input, such as image recognition or speech-to-text translation. However, deep learning is not particularly good when it comes to problems with... 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

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

Representation Learning

Learning meaningful, low-dimensional representations of data is a challenging problem. Particularly for an autonomously learning system, representations learned from observations can play a crucial role. Consider for example a system... Read more

Stochastic gradient methods are the driving force behind the recent boom of deep learning. As a result, the demand for practical efficiency as well as for theoretical understanding has never been stronger. Naturally, this has inspired a... Read more

In this project, we investigate learning probabilistic neural networks, that is, neural networks that output an uncertainty-aware prediction. Specifically, we are interested in modeling aleatoric uncertainty, which is the uncertainty... Read more

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

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