| Clustering with deep learning: Taxonomy and new methods E Aljalbout, V Golkov, Y Siddiqui, M Strobel, D Cremers arXiv preprint arXiv:1801.07648, 2018 | 351 | 2018 |
| Associative Deep Clustering: Training a Classification Network with no Labels P Haeusser, J Plapp, V Golkov, E Aljalbout, D Cremers German Conference on Pattern Recognition, 2018 | 152 | 2018 |
| Tracking Holistic Object Representations A Sauer*, E Aljalbout*, S Haddadin British Machine Vision Conference (BMVC), 2019 | 52 | 2019 |
| Clustering with deep learning: taxonomy and new methods (2018) E Aljalbout, V Golkov, Y Siddiqui, D Cremers arXiv preprint arXiv:1801.07648, 2018 | 31* | 2018 |
| Learning vision-based reactive policies for obstacle avoidance E Aljalbout, J Chen, K Ritt, M Ulmer, S Haddadin Conference on Robot Learning, 2040-2054, 2021 | 25 | 2021 |
| 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 IEEE Robotics and Automation Letters, 2024 | 22 | 2024 |
| Learning to Centralize Dual-Arm Assembly M Alles, E Aljalbout Frontiers in Robotics and AI, 2022 | 18 | 2022 |
| How to Make Deep RL Work in Practice N Rao*, E Aljalbout*, A Sauer*, S Haddadin Deep RL Workshop, Conference on Neural Information Processing Systems (NeurIPS), 2020 | 13 | 2020 |
| Learning robotic manipulation skills using an adaptive force-impedance action space M Ulmer, E Aljalbout, S Schwarz, S Haddadin arXiv preprint arXiv:2110.09904, 2021 | 8 | 2021 |
| Task-independent spiking central pattern generator: A learning-based approach E Aljalbout, F Walter, F Röhrbein, A Knoll Neural Processing Letters 51, 2751-2764, 2020 | 7 | 2020 |
| Clas: Coordinating multi-robot manipulation with central latent action spaces E Aljalbout, M Karl, P van der Smagt Learning for Dynamics and Control Conference, 1152-1166, 2023 | 6 | 2023 |
| Tracking holistic object representations. arXiv 2019 A Sauer, E Aljalbout, S Haddadin arXiv preprint arXiv:1907.12920, 0 | 6 | |
| Seeking visual discomfort: Curiosity-driven representations for reinforcement learning E Aljalbout, M Ulmer, R Triebel 2022 International Conference on Robotics and Automation (ICRA), 3591-3597, 2022 | 3 | 2022 |
| Making Curiosity Explicit in Vision-based RL E Aljalbout*, M Ulmer*, R Triebel 2021 IEEE International Conference on Robotics and Automation (ICRA …, 2021 | 3 | 2021 |
| Tracking holistic object representations. arXiv A Sauer, E Aljalbout, S Haddadin arXiv preprint arXiv:1907.12920, 2019 | 3 | 2019 |
| Limt: Language-informed multi-task visual world models E Aljalbout, N Sotirakis, P van der Smagt, M Karl, N Chen 2025 International Conference on Robotics and Automation (ICRA), 2025 | 2 | 2025 |
| Multi-Task Reinforcement Learning for Quadrotors J Xing, I Geles, Y Song, E Aljalbout, D Scaramuzza IEEE Robotics and Automation Letters, 2024 | 1 | 2024 |
| GEM: A Generalizable Ego-Vision Multimodal World Model for Fine-Grained Ego-Motion, Object Dynamics, and Scene Composition Control M Hassan, S Stapf, A Rahimi, P Rezende, Y Haghighi, D Brüggemann, ... arXiv preprint arXiv:2412.11198, 2024 | 1 | 2024 |
| Actor-Critic Model Predictive Control: Differentiable Optimization meets Reinforcement Learning A Romero, E Aljalbout, Y Song, D Scaramuzza | 1 | 2024 |
| Dual-Arm Adversarial Robot Learning E Aljalbout 5th Annual Conference on Robot Learning, Blue Sky Submission Track, 2022 | 1 | 2022 |
Daniel CremersTechnical University of MunichAdresse e-mail validée de tum.de
