3 papers
cs.RO2025
Context-Aware Deep Lagrangian Networks for Model Predictive Control
Lucas Schulze, Jan Peters, Oleg Arenz
Controlling a robot based on physics-consistent dynamic models, such as Deep Lagrangian Networks (DeLaN), can improve the generalizability and interpretability of the resulting beh…
cs.LG2025
Maximum Total Correlation Reinforcement Learning
Bang You, Puze Liu, Huaping Liu +2
Simplicity is a powerful inductive bias. In reinforcement learning, regularization is used for simpler policies, data augmentation for simpler representations, and sparse reward fu…
cs.RO2025
Learning Hierarchical Domain Models Through Environment-Grounded Interaction
Claudius Kienle, Benjamin Alt, Oleg Arenz +1
Domain models enable autonomous agents to solve long-horizon tasks by producing interpretable plans. However, in open-world environments, a single general domain model cannot captu…