| RMA: Rapid motor adaptation for legged robots A Kumar, Z Fu, D Pathak, J Malik Robotics: Science and Systems, 2021 | 345 | 2021 |
| Energy theft detection with energy privacy preservation in the smart grid D Yao, M Wen, X Liang, Z Fu, K Zhang, B Yang IEEE Internet of Things Journal 6 (5), 7659-7669, 2019 | 194 | 2019 |
| Machine learning for glass science and engineering: A review H Liu, Z Fu, K Yang, X Xu, M Bauchy Journal of Non-Crystalline Solids 557, 119419, 2021 | 137 | 2021 |
| Minimizing energy consumption leads to the emergence of gaits in legged robots Z Fu, A Kumar, J Malik, D Pathak Conference on Robot Learning, 2021 | 79 | 2021 |
| Open X-Embodiment: Robotic learning datasets and rt-x models A Padalkar, A Pooley, A Jain, A Bewley, A Herzog, A Irpan, A Khazatsky, ... International Conference on Robotics and Automation, 2023 | 70 | 2023 |
| Deep whole-body control: learning a unified policy for manipulation and locomotion Z Fu, X Cheng, D Pathak Conference on Robot Learning, 138-149, 2022 | 62 | 2022 |
| Coupling vision and proprioception for navigation of legged robots Z Fu, A Kumar, A Agarwal, H Qi, J Malik, D Pathak Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 48 | 2022 |
| Robot parkour learning Z Zhuang, Z Fu, J Wang, C Atkeson, S Schwertfeger, C Finn, H Zhao Conference on Robot Learning, 2023 | 35 | 2023 |
| Mobile ALOHA: Learning bimanual mobile manipulation with low-cost whole-body teleoperation Z Fu, TZ Zhao, C Finn arXiv preprint arXiv:2401.02117, 2024 | 30 | 2024 |
| Balance between accuracy and simplicity in empirical forcefields for glass modeling: insights from machine learning H Liu, Z Fu, Y Li, NFA Sabri, M Bauchy Journal of Non-Crystalline Solids 515, 133-142, 2019 | 21 | 2019 |
| Parameterization of empirical forcefields for glassy silica using machine learning H Liu, Z Fu, Y Li, NFA Sabri, M Bauchy MRS Communications 9 (2), 593-599, 2019 | 20 | 2019 |
| Emergence of pragmatics from referential game between theory of mind agents L Yuan, Z Fu, J Shen, L Xu, J Shen, SC Zhu NeurIPS 2019 Emergent Communication Workshop, 2020 | 19 | 2020 |
| Exploring the landscape of Buckingham potentials for silica by machine learning: Soft vs hard interatomic forcefields H Liu, Y Li, Z Fu, K Li, M Bauchy The Journal of Chemical Physics 152 (5), 2020 | 15 | 2020 |
| Machine learning forcefield for silicate glasses H Liu, Z Fu, Y Li, NFA Sabri, M Bauchy arXiv preprint arXiv:1902.03486, 2019 | 12 | 2019 |
| Emergence of Theory of Mind Collaboration in Multiagent Systems L Yuan, Z Fu, L Zhou, K Yang, SC Zhu NeurIPS 2019 Emergent Communication Workshop, 2020 | 8 | 2020 |
| Reducing overestimation in value mixing for cooperative deep multi-agent reinforcement learning Z Fu, Q Zhao, W Zhang Proceedings of the International Conference on Agents and Artificial …, 2020 | 5 | 2020 |
| Adversarial attack against scene recognition system for unmanned vehicles X Wang, M Wen, J Li, Z Fu, R Lu, K Chen Proceedings of the ACM Turing Celebration Conference-China, 1-6, 2019 | 2 | 2019 |
| Multi-Modal Imitation Learning in Partially Observable Environments Z Fu, M Liu, M Zhou, W Zhang | 1 | 2020 |
| Adapt On-the-Go: Behavior Modulation for Single-Life Robot Deployment AS Chen, G Chada, L Smith, A Sharma, Z Fu, S Levine, C Finn arXiv preprint arXiv:2311.01059, 2023 | | 2023 |
Deepak PathakAssistant Professor, Carnegie Mellon UniversityVerified email at cs.cmu.edu
