| Deep reinforcement learning with successor features for navigation across similar environments J Zhang, JT Springenberg, J Boedecker, W Burgard 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2017 | 313 | 2017 |
| Socially compliant navigation through raw depth inputs with generative adversarial imitation learning L Tai, J Zhang, M Liu, W Burgard 2018 IEEE international conference on robotics and automation (ICRA), 1111-1117, 2018 | 233 | 2018 |
| Neural slam: Learning to explore with external memory J Zhang, L Tai, J Boedecker, W Burgard, M Liu arXiv preprint arXiv:1706.09520, 2017 | 181 | 2017 |
| Genie: Generative interactive environments J Bruce, MD Dennis, A Edwards, J Parker-Holder, Y Shi, E Hughes, M Lai, ... Forty-first International Conference on Machine Learning, 2024 | 143 | 2024 |
| Vr-goggles for robots: Real-to-sim domain adaptation for visual control J Zhang, L Tai, P Yun, Y Xiong, M Liu, J Boedecker, W Burgard IEEE Robotics and Automation Letters 4 (2), 1148-1155, 2019 | 143 | 2019 |
| Curiosity-driven exploration for mapless navigation with deep reinforcement learning O Zhelo, J Zhang, L Tai, M Liu, W Burgard arXiv preprint arXiv:1804.00456, 2018 | 134 | 2018 |
| A survey of deep network solutions for learning control in robotics: From reinforcement to imitation L Tai, J Zhang, M Liu, J Boedecker, W Burgard arXiv preprint arXiv:1612.07139, 2016 | 101 | 2016 |
| Deep reinforcement learning with successor features for navigation across similar environments. In 2017 IEEE J Zhang, JT Springenberg, J Boedecker, W Burgard RSJ International Conference on Intelligent Robots and Systems (IROS), 2371-2378, 0 | 53 | |
| A generalist dynamics model for control I Schubert, J Zhang, J Bruce, S Bechtle, E Parisotto, M Riedmiller, ... arXiv preprint arXiv:2305.10912, 2023 | 29 | 2023 |
| Attend2pack: Bin packing through deep reinforcement learning with attention J Zhang, B Zi, X Ge arXiv preprint arXiv:2107.04333, 2021 | 29 | 2021 |
| Scheduled intrinsic drive: A hierarchical take on intrinsically motivated exploration J Zhang, N Wetzel, N Dorka, J Boedecker, W Burgard arXiv preprint arXiv:1903.07400, 2019 | 27 | 2019 |
| Efficiency and equity are both essential: A generalized traffic signal controller with deep reinforcement learning S Yan, J Zhang, D Büscher, W Burgard 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2020 | 13 | 2020 |
| Offline actor-critic reinforcement learning scales to large models JT Springenberg, A Abdolmaleki, J Zhang, O Groth, M Bloesch, T Lampe, ... arXiv preprint arXiv:2402.05546, 2024 | 12 | 2024 |
| Perspectives on Deep Multimodel Robot Learning W Burgard, A Valada, N Radwan, T Naseer, J Zhang, J Vertens, O Mees, ... | 11* | |
| Leveraging jumpy models for planning and fast learning in robotic domains J Zhang, JT Springenberg, A Byravan, L Hasenclever, A Abdolmaleki, ... arXiv preprint arXiv:2302.12617, 2023 | 7 | 2023 |
| Supplement file of VR-Goggles for robots: Real-to-sim domain adaptation for visual control J Zhang, L Tai, PYYXM Liu, JBW Burgard Training 853 (840), 715, 2018 | 4 | 2018 |
| Mastering stacking of diverse shapes with large-scale iterative reinforcement learning on real robots T Lampe, A Abdolmaleki, S Bechtle, SH Huang, JT Springenberg, ... 2024 IEEE International Conference on Robotics and Automation (ICRA), 7772-7779, 2024 | 3 | 2024 |
| Equivariant data augmentation for generalization in offline reinforcement learning C Pinneri, S Bechtle, M Wulfmeier, A Byravan, J Zhang, WF Whitney, ... arXiv preprint arXiv:2309.07578, 2023 | 3 | 2023 |
| Game On: Towards Language Models as RL Experimenters J Zhang, T Lampe, A Abdolmaleki, JT Springenberg, M Riedmiller arXiv preprint arXiv:2409.03402, 2024 | | 2024 |
| pytorch-dnc J Zhang https://github.com/jingweiz/pytorch-dnc, 2017 | | 2017 |
Wolfram BurgardProfessor of Computer Science, Technical University of NurembergVerified email at utn.de
