| Deep variational bayes filters: Unsupervised learning of state space models from raw data M Karl, M Soelch, J Bayer, P van der Smagt arXiv preprint arXiv:1605.06432, 2016 | 451 | 2016 |
| Stable reinforcement learning with autoencoders for tactile and visual data H van Hoof, N Chen, M Karl, P van der Smagt, J Peters 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2016 | 193 | 2016 |
| Dynamic movement primitives in latent space of time-dependent variational autoencoders N Chen, M Karl, P van der Smagt 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids …, 2016 | 75 | 2016 |
| Unsupervised real-time control through variational empowerment M Karl, P Becker-Ehmck, M Soelch, D Benbouzid, P Smagt, J Bayer The International Symposium of Robotics Research, 158-173, 2019 | 60 | 2019 |
| Learning to Fly via Deep Model-Based Reinforcement Learning P Becker-Ehmck, M Karl, J Peters, P van der Smagt arXiv preprint arXiv:2003.08876, 2020 | 46 | 2020 |
| Variational Inference with Hamiltonian Monte Carlo C Wolf, M Karl, P van der Smagt arXiv preprint arXiv:1609.08203, 2016 | 44 | 2016 |
| On the role of the action space in robot manipulation learning and sim-to-real transfer E Aljalbout, F Frank, M Karl, P van der Smagt arXiv preprint arXiv:2312.03673, 2023 | 17 | 2023 |
| Efficient Empowerment M Karl, J Bayer, P van der Smagt arXiv preprint arXiv:1509.08455, 2015 | 12 | 2015 |
| Continuum worm-like robotic mechanism with decentral control architecture M Eder, M Karl, A Knoll, S Riesner 2014 IEEE International Conference on Automation Science and Engineering …, 2014 | 9 | 2014 |
| Exploration via Empowerment Gain: Combining Novelty, Surprise and Learning Progress P Becker-Ehmck, M Karl, J Peters, P van der Smagt ICML 2021 Workshop on Unsupervised Reinforcement Learning, 2021 | 6 | 2021 |
| Fast adaptive weight noise J Bayer, M Karl, D Korhammer, P van der Smagt arXiv preprint arXiv:1507.05331, 2015 | 6 | 2015 |
| CLAS: Coordinating Multi-Robot Manipulation with Central Latent Action Spaces E Aljalbout, M Karl, P van der Smagt arXiv preprint arXiv:2211.15824, 2022 | 5 | 2022 |
| Beta DVBF: Learning State-Space Models for Control from High Dimensional Observations N Das, M Karl, P Becker-Ehmck, P van der Smagt arXiv preprint arXiv:1911.00756, 2019 | 5 | 2019 |
| Action Inference by Maximising Evidence: Zero-Shot Imitation from Observation with World Models X Zhang, P Becker-Ehmck, P van der Smagt, M Karl Workshop on Reincarnating Reinforcement Learning at ICLR 2023, 2023 | 4 | 2023 |
| Unsupervised preprocessing for Tactile Data M Karl, J Bayer, P van der Smagt arXiv preprint arXiv:1606.07312, 2016 | 4 | 2016 |
| Design of an inherently safe worm-like robot M Eder, M Karl, F Schultheiß, J Schürmann, A Knoll, S Riesner 2013 IEEE International Symposium on Safety, Security, and Rescue Robotics …, 2013 | 4 | 2013 |
| Compliant worm-like robotic mechanism with decentrally controlled pneumatic artificial muscles M Eder, M Karl, A Knoll, S Riesner 2012 First International Conference on Innovative Engineering Systems, 243-248, 2012 | 4 | 2012 |
| ML-based tactile sensor calibration: A universal approach M Karl, A Lohrer, D Shah, F Diehl, M Fiedler, S Ognawala, J Bayer, ... arXiv preprint arXiv:1606.06588, 2016 | 2 | 2016 |
| Improving approximate RPCA with a K-sparsity prior M Karl, C Osendorfer arXiv preprint arXiv:1412.8291, 2014 | 2 | 2014 |
| Overcoming Knowledge Barriers: Online Imitation Learning from Observation with Pretrained World Models X Zhang, P Becker-Ehmck, P van der Smagt, M Karl arXiv preprint arXiv:2404.18896, 2024 | | 2024 |
