| Long-horizon active SLAM system for multi-agent coordinated exploration M Ossenkopf, G Castro, F Pessacg, K Geihs, P De Cristóforis 2019 European Conference on Mobile Robots (ECMR), 1-6, 2019 | 16 | 2019 |
| When does communication learning need hierarchical multi-agent deep reinforcement learning M Ossenkopf, M Jorgensen, K Geihs Cybernetics and Systems 50 (8), 672-692, 2019 | 8 | 2019 |
| Hierarchical multi-agent deep reinforcement learning to develop long-term coordination M Ossenkopf, M Jorgensen, K Geihs Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, 922-929, 2019 | 5 | 2019 |
| Reinforcement learning for manipulators without direct obstacle perception in physically constrained environments M Ossenkopf, P Ennen, R Vossen, S Jeschke Procedia Manufacturing 11, 329-337, 2017 | 5 | 2017 |
| Which language evolves between heterogeneous agents?-communicating movement instructions with widely different time scopes M Ossenkopf, KS Luck, KW Mathewson Emergent Communication Workshop at ICLR 2022, 2022 | 3 | 2022 |
| Positioning of active wheels for optimal ball handling: a guide for designing a new ball handle mechanism for Middle-Size-League at RoboCup M Beuermann, M Ossenkopf, K Geihs RoboCup 2019: Robot World Cup XXIII 23, 30-43, 2019 | 2 | 2019 |
| Robotic education in the DLR_SCHOOL_LAB RWTH AACHEN S Reuter, K Yilanci, N Nabil, M Ossenkopf, F Reinbacher, J Schlierkamp, ... INTED2015 Proceedings, 3466-3475, 2015 | 2 | 2015 |
| Student Competition Teams: Combining Research and Teaching S Opfer, M Ossenkopf, K Geihs Southern African Computer Lecturers’ Association (SACLA 2018), 248, 2018 | 1 | 2018 |
| Learning planning communication in cooperative multi-agent settings M Ossenkopf | | 2023 |
| Enhancing Language Emergence through Empathy M Ossenkopf | | |
