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publications
Clustering with Deep Learning: Taxonomy and New Methods
Elie Aljalbout, Vladimir Golkov, Yawar Siddiqui, Maximilian Strobel, and Daniel Cremers arXiv, 2018 [ pdf ]
Associative deep clustering: Training a classification network with no labels
Philip Haeusser, Johannes Plapp, Vladimir Golkov, Elie Aljalbout, and Daniel Cremers GCPR, 2018 [ pdf ]
Tracking Holistic Object Representations
Axel Sauer*, Elie Aljalbout*, Sami Haddadin (*Shared first authorship) BMVC (oral, acceptance rate: 4%), **Best Science Paper Award**, 2019 [ pdf | url ]
Task-Independent Spiking Central Pattern Generator: A Learning-Based Approach
Elie Aljalbout, Florian Walter, Florian Roehrbein, and Alois Knoll Neural Processing Letters, Springer, 2020 [ pdf | url ]
How to Make Deep RL Work in Practice
Nirnai Rao*, Elie Aljalbout*, Axel Sauer*, Sami Haddadin (*Shared first authorship) NeurIPS, Workshop on Deep RL, 2020 [ pdf | url ]
Learning Vision-based Reactive Policies for Obstacle Avoidance
Elie Aljalbout, Ji Chen, Konstantin Ritt, Maximilian Ulmer and Sami Haddadin CoRL, 2020 [ pdf | url ]
Learning robotic manipulation skills using an adaptive force-impedance action space
Maximilian Ulmer, Elie Aljalbout, Sascha Schwarz, Sami Haddadin arXiv, 2021 [ pdf ]
Seeking Visual Discomfort: Curiosity-driven Representations for Reinforcement Learning
Elie Aljalbout, Maximilian Ulmer, Rudolph Triebel ICRA, 2022 [ pdf | url ]
Learning to Centralize Dual-Arm Assembly
Marvin Alles, Elie Aljalbout Frontiers in Robotics and AI, 2022 [ pdf | url ]
CLAS: Coordinating Multi-Robot Manipulation with Central Latent Action Spaces
Elie Aljalbout, Maximilian Karl, Patrick van der Smagt L4DC, 2023 [ pdf | url ]
The Shortcomings of Force-from-Motion in Robot Learning
Elie Aljalbout, Felix Frank, Patrick van der Smagt, Alexandros Paraschos ICRA, 2024 [ pdf ]
On the Role of the Action Space in Robot Manipulation Learning and Sim-to-Real Transfer
Elie Aljalbout*, Felix Frank*, Maximilian Karl, Patrick van der Smagt RA-L, 2024 [ pdf | url ]
talks
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