Philip Becker-Ehmck

According to our database, Philip Becker-Ehmck authored at least 9 papers between 2019 and 2025.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2025

Latent Action World Models for Control with Unlabeled Trajectories.

Marvin Alles, Xingyuan Zhang, Patrick van der Smagt, Philip Becker-Ehmck

CoRR, December, 2025

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

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

Constrained Latent Action Policies for Model-Based Offline Reinforcement Learning.

Marvin Alles, Philip Becker-Ehmck, Patrick van der Smagt, Maximilian Karl

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

2023

Action Inference by Maximising Evidence: Zero-Shot Imitation from Observation with World Models.

Xingyuan Zhang, Philip Becker-Ehmck, Patrick van der Smagt, Maximilian Karl

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2020

Learning to Fly via Deep Model-Based Reinforcement Learning.

Philip Becker-Ehmck, Maximilian Karl, Jan Peters, Patrick van der Smagt

CoRR, 2020

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

Unsupervised Real-Time Control Through Variational Empowerment.

Maximilian Karl, Philip Becker-Ehmck, Maximilian Soelch, Djalel Benbouzid, Patrick van der Smagt, Justin Bayer

Proceedings of the Robotics Research, 2019

Switching Linear Dynamics for Variational Bayes Filtering.

Philip Becker-Ehmck, Jan Peters, Patrick van der Smagt

Proceedings of the 36th International Conference on Machine Learning, 2019