M. Sc. Christian Gumbsch

I studied Cognitive Science (B.Sc. 2013 and M.Sc. 2018) at the University of Tübingen. Subsequently, I was a research intern and later a doctoral researcher at the Max Planck Institute for Intelligent Systems and the University of Tübingen supervised by Prof. Georg Martius and Prof. Martin Butz. During my doctoral studies I researched how artificial agents can develop temporal abstractions of their sensorimotor experience for hierarchical prediction and planning. My doctoral research followed a highly interdisciplinary approach combining insights from various fields of Cognitive Science, including cognitive and developmental psychology, with deep learning techniques, such as deep reinforcement learning. I joined the Chair of Cognitive and Clinical Neuroscience at the TU Dresden in 2023 to research the encoding of contextual information from multimodal sensory data.

My long-term research goal is to help the development of autonomous agents that learn adaptive goal-directed behavior purely from their interactions with the world, like humans or other animals. To work towards this goal, I follow two research directions that interact bidirectionally: 1. I research inductive biases and computational mechanisms that improve the autonomous learning and problem-solving capabilities of artificial agents. 2. I develop computational cognitive models to research the mechanisms and representations that give rise to the adaptive behavior found in natural agents. Thus, I have a broad research interest in various topic including sequential decision making, hierarchical and multimodal representation learning, and causal discovery in both natural and artificial agents.

Gumbsch, C., Adam, M., Elsner, B., Martius, G., & Butz, M. V. (2022). Developing hierarchical anticipations via neural network-based event segmentation. In 2022 IEEE International Conference on Development and Learning (ICDL), 1-8. IEEE. https://arxiv.org/abs/2206.02042

Eppe, M., Gumbsch, C., Kerzel, M., Nguyen, P. D., Butz, M. V., & Wermter, S. (2022). Intelligent problem-solving as integrated hierarchical reinforcement learning. Nature Machine Intelligence, 4(1), 11-20. https://rdcu.be/cFGsE

Gumbsch, C., Butz, M. V., & Martius, G. (2021). Sparsely changing latent states for prediction and planning in partially observable domains. Advances in Neural Information Processing Systems (NeurIPS), 34, 17518-17531. https://openreview.net/pdf?id=-VjKyYX-PI9

Gumbsch, C., Adam, M., Elsner, B., & Butz, M. V. (2021). Emergent Goal‐Anticipatory Gaze in Infants via Event‐Predictive Learning and Inference. Cognitive Science, 45(8), e13016. https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/cogs.13016

Gumbsch, C., Butz, M. V., & Martius, G. (2019). Autonomous identification and goal-directed invocation of event-predictive behavioral primitives. IEEE transactions on cognitive and developmental systems, 13(2), 298-311. https://arxiv.org/pdf/1902.09948.pdf