| See, feel, act: Hierarchical learning for complex manipulation skills with multisensory fusion N Fazeli, M Oller, J Wu, Z Wu, JB Tenenbaum, A Rodriguez Science Robotics 4 (26), eaav3123, 2019 | 137 | 2019 |
| Efficient sampling-based maximum entropy inverse reinforcement learning with application to autonomous driving Z Wu, L Sun, W Zhan, C Yang, M Tomizuka IEEE Robotics and Automation Letters 5 (4), 5355-5362, 2020 | 108 | 2020 |
| Learning to describe scenes with programs Y Liu, Z Wu, D Ritchie, WT Freeman, JB Tenenbaum, J Wu International Conference on Learning Representations (ICLR), 2019, 2018 | 61 | 2018 |
| Offline-online learning of deformation model for cable manipulation with graph neural networks C Wang, Y Zhang, X Zhang, Z Wu, X Zhu, S Jin, T Tang, M Tomizuka IEEE Robotics and Automation Letters 7 (2), 5544-5551, 2022 | 55 | 2022 |
| Learning Dense Rewards for Contact-Rich Manipulation Tasks Z Wu, W Lian, V Unhelkar, M Tomizuka, S Schaal 2021 International Conference on Robotics and Automation (ICRA), 2020 | 40 | 2020 |
| Annotation-Free and One-Shot Learning for Instance Segmentation of Homogeneous Object Clusters Z Wu, R Chang, J Ma, C Lu, CK Tang International Joint Conference on Artificial Intelligence (IJCAI), 2018, 2018 | 14 | 2018 |
| Efficient sim-to-real transfer of contact-rich manipulation skills with online admittance residual learning X Zhang, C Wang, L Sun, Z Wu, X Zhu, M Tomizuka Conference on Robot Learning, 1621-1639, 2023 | 12 | 2023 |
| Prim-lafd: A framework to learn and adapt primitive-based skills from demonstrations for insertion tasks Z Wu, W Lian, C Wang, M Li, S Schaal, M Tomizuka IFAC-PapersOnLine 56 (2), 4120-4125, 2023 | 11 | 2023 |
| Expressing Diverse Human Driving Behavior with Probabilistic Rewards and Online Inference L Sun, Z Wu, H Ma, M Tomizuka IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020 | 8 | 2020 |
| Efficient Reinforcement Learning of Task Planners for Robotic Palletization through Iterative Action Masking Learning Z Wu, Y Li, W Zhan, C Liu, YH Liu, M Tomizuka arXiv preprint arXiv:2404.04772, 2024 | 3 | 2024 |
| Zero-Shot Policy Transfer with Disentangled Task Representation of Meta-Reinforcement Learning Z Wu, Y Xie, W Lian, C Wang, Y Guo, J Chen, S Schaal, M Tomizuka 2023 International Conference on Robotics and Automation (ICRA), 2022 | 3 | 2022 |
| Pearl: A Production-ready Reinforcement Learning Agent Z Zhu, RS Braz, J Bhandari, D Jiang, Y Wan, Y Efroni, L Wang, R Xu, ... arXiv preprint arXiv:2312.03814, 2023 | 2 | 2023 |
| Reinforcement learning with Demonstrations from Mismatched Task under Sparse Reward Y Guo, J Gao, Z Wu, C Shi, J Chen Conference on Robot Learning, 1146-1156, 2023 | 2 | 2023 |
| DBPF: A Framework for Efficient and Robust Dynamic Bin-Picking Y Li, J Zhao, Y Li, Z Wu, R Cao, M Tomizuka, YH Liu IEEE Robotics and Automation Letters, 2024 | | 2024 |
| LEARNING TO ACQUIRE AND ADAPT CONTACT-RICH MANIPULATION SKILLS WITH MOTION PRIMITIVES W Lian, S Schaal, Z Wu US Patent App. 17/845,698, 2022 | | 2022 |
Masayoshi TomizukaMechaniccal Engineering, University of CaliforniaVerified email at me.berkeley.edu
