93 citations · 105 across the 12 of their papers we have counts for
12 papers
From Demonstrations to Rewards: Test-Time Prompt Optimization for VLM Reward Models
Christian Gumbsch, Leonardo Barcellona, Lennard Schünemann +7
Reinforcement learning relies on accurate reward functions, which are often hand-crafted or even unavailable in real-world applications, such as robotics. Recent work has explored…
Reconstruction by Generation: 3D Multi-Object Scene Reconstruction from Sparse Observations
Andrii Zadaianchuk, Leonardo Barcellona, Lennard Schuenemann +7
Accurately reconstructing complex full multi-object scenes from sparse observations remains a core challenge in computer vision and a key step toward scalable and reliable simulati…
Causal Process Models: Reframing Dynamic Causal Graph Discovery as a Reinforcement Learning Problem
Turan Orujlu, Christian Gumbsch, Martin V. Butz +1
Most neural models of causality assume static causal graphs, failing to capture the dynamic and sparse nature of physical interactions where causal relationships emerge and dissolv…
Investigating Pedagogical Teacher and Student LLM Agents: Genetic Adaptation Meets Retrieval Augmented Generation Across Learning Style
Debdeep Sanyal, Agniva Maiti, Umakanta Maharana +4
Effective teaching requires adapting instructional strategies to accommodate the diverse cognitive and behavioral profiles of students, a persistent challenge in education and teac…
SENSEI: Semantic Exploration Guided by Foundation Models to Learn Versatile World Models
Cansu Sancaktar, Christian Gumbsch, Andrii Zadaianchuk +2
Exploration is a cornerstone of reinforcement learning (RL). Intrinsic motivation attempts to decouple exploration from external, task-based rewards. However, established approache…
Intelligent problem-solving as integrated hierarchical reinforcement learning
Manfred Eppe, Christian Gumbsch, Matthias Kerzel +3
According to cognitive psychology and related disciplines, the development of complex problem-solving behaviour in biological agents depends on hierarchical cognitive mechanisms. H…
Developing hierarchical anticipations via neural network-based event segmentation
Christian Gumbsch, Maurits Adam, Birgit Elsner +2
Humans can make predictions on various time scales and hierarchical levels. Thereby, the learning of event encodings seems to play a crucial role. In this work we model the develop…
Inference of Affordances and Active Motor Control in Simulated Agents
Fedor Scholz, Christian Gumbsch, Sebastian Otte +1
Flexible, goal-directed behavior is a fundamental aspect of human life. Based on the free energy minimization principle, the theory of active inference formalizes the generation of…
Sparsely Changing Latent States for Prediction and Planning in Partially Observable Domains
Christian Gumbsch, Martin V. Butz, Georg Martius
A common approach to prediction and planning in partially observable domains is to use recurrent neural networks (RNNs), which ideally develop and maintain a latent memory about hi…
Latent Event-Predictive Encodings through Counterfactual Regularization
Dania Humaidan, Sebastian Otte, Christian Gumbsch +2
A critical challenge for any intelligent system is to infer structure from continuous data streams. Theories of event-predictive cognition suggest that the brain segments sensorimo…
Hierarchical principles of embodied reinforcement learning: A review
Manfred Eppe, Christian Gumbsch, Matthias Kerzel +3
Cognitive Psychology and related disciplines have identified several critical mechanisms that enable intelligent biological agents to learn to solve complex problems. There exists…
Autonomous Identification and Goal-Directed Invocation of Event-Predictive Behavioral Primitives
Christian Gumbsch, Martin V. Butz, Georg Martius
Voluntary behavior of humans appears to be composed of small, elementary building blocks or behavioral primitives. While this modular organization seems crucial for the learning of…