Switching Linear Dynamics for Variational Bayes Filtering. - Philip Becker-Ehmck; Jan Peters 001; Patrick van der Smagt (ICML, 2019)

Unsupervised Real-Time Control Through Variational Empowerment. - Maximilian Karl; Philip Becker-Ehmck; Maximilian Soelch 001; Djalel Benbouzid; Patrick van der Smagt; Justin Bayer (ISRR, 2019)

Beta DVBF: Learning State-Space Models for Control from High Dimensional Observations. - Neha Das; Maximilian Karl; Philip Becker-Ehmck; Patrick van der Smagt (CoRR, 2019)

Learning to Fly via Deep Model-Based Reinforcement Learning. - Philip Becker-Ehmck; Maximilian Karl; Jan Peters 001; Patrick van der Smagt (CoRR, 2020)

Action Inference by Maximising Evidence: Zero-Shot Imitation from Observation with World Models. - Xingyuan Zhang; Philip Becker-Ehmck; Patrick van der Smagt; Maximilian Karl (NeurIPS, 2023)

Constrained Latent Action Policies for Model-Based Offline Reinforcement Learning. - Marvin Alles; Philip Becker-Ehmck; Patrick van der Smagt; Maximilian Karl (NeurIPS, 2024)

Overcoming Knowledge Barriers: Online Imitation Learning from Observation with Pretrained World Models. - Xingyuan Zhang; Philip Becker-Ehmck; Patrick van der Smagt; Maximilian Karl (CoRR, 2024)

Overcoming Knowledge Barriers: Online Imitation Learning from Visual Observation with Pretrained World Models. - Xingyuan Zhang; Philip Becker-Ehmck; Patrick van der Smagt; Maximilian Karl (Trans. Mach. Learn. Res., 2025)