Several MCML PIs Receive BMBF Funding

The BMBF is funding two new joint projects with several MCML researchers involved. One teaches AI models causal relationships, the other refines the tactile abilities of robots.

CausalNet: AI that Understands Cause and Effect
The CausalNet project aims to advance machine learning (ML) by integrating causal reasoning, moving beyond current models that rely solely on correlations. This approach promises greater reliability and performance, particularly in fields like medicine, where understanding cause-and-effect relationships could enable targeted therapies.

Funded with nearly €2 million by the German Federal Ministry of Education and Research (BMBF), CausalNet will develop novel methods for embedding causality into ML, working with experts from LMU, TUM, KIT, Helmholtz AI, and Economic AI GmbH – including MCML PIs Stefan Feuerriegel (spokesperson of the project), Stefan Bauer, and Niki Kilbertus. The project will tackle challenges in high-dimensional environments using tools from representation learning, statistical efficiency, and specialized ML paradigms, with a focus on open-source outputs.

GeniusRobot: Enhancing Robotic Vision and Grasping with AI
The other project focuses on improving robotic manipulation using generative AI. MCML PIs Gitta Kutyniok and Björn Ommer are developing interpretable AI models that predict tactile information from visual data, enabling robots to dynamically adapt their grip.

The project leverages multimodal AI to integrate and interpret sensory inputs, enhancing robotic flexibility and resilience. It also explores converting tactile data back into visualizations, aiding manipulation of partially visible objects. The research aims to unlock new use cases in automated manufacturing and human-machine interaction, prioritizing safety and interpretability in critical environments.

Congrats to everyone involved!