| Concept2Robot: Learning Manipulation Concepts from Instructions and Human Demonstrations L Shao, T Migimatsu, Q Zhang, K Yang, J Bohg Proceedings of Robotics: Science and Systems (RSS) 2020, 2020 | 198 | 2020 |
| UniGrasp: Learning a Unified Model to Grasp with Multifingered Robotic Hands L Shao, F Ferreira, M Jorda, V Nambiar, J Luo, E Solowjow, JA Ojea, ... arXiv preprint arXiv:1910.10900, 2019 | 120 | 2019 |
| Generative 3d part assembly via dynamic graph learning G Zhan, Q Fan, K Mo, L Shao, B Chen, LJ Guibas, H Dong Advances in Neural Information Processing Systems 33, 6315-6326, 2020 | 87 | 2020 |
| Large-scale 3d shape reconstruction and segmentation from shapenet core55 L Yi, L Shao, M Savva, H Huang, Y Zhou, Q Wang, B Graham, M Engelcke, ... arXiv preprint arXiv:1710.06104, 2017 | 75 | 2017 |
| Learning 3d part assembly from a single image Y Li, K Mo, L Shao, M Sung, L Guibas Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 71 | 2020 |
| Design and control of roller grasper v2 for in-hand manipulation S Yuan, L Shao, CL Yako, A Gruebele, JK Salisbury 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2020 | 54 | 2020 |
| Motion-based Object Segmentation based on Dense RGB-D Scene Flow L Shao, P Shah, V Dwaracherla, J Bohg arXiv preprint arXiv:1804.05195, 2018 | 49 | 2018 |
| Learning to Scaffold the Development of Robotic Manipulation Skills L Shao, T Migimatsu, J Bohg 2020 IEEE International Conference on Robotics and Automation (ICRA), 5671-5677, 2020 | 33 | 2020 |
| Sagci-system: Towards sample-efficient, generalizable, compositional, and incremental robot learning J Lv, Q Yu, L Shao, W Liu, W Xu, C Lu 2022 International Conference on Robotics and Automation (ICRA), 98-105, 2022 | 27 | 2022 |
| Grac: Self-guided and self-regularized actor-critic L Shao, Y You, M Yan, S Yuan, Q Sun, J Bohg Conference on Robot Learning, 267-276, 2022 | 27 | 2022 |
| ClusterNet: 3D Instance Segmentation in RGB-D Images L Shao, Y Tian, J Bohg arXiv preprint arXiv:1807.08894, 2018 | 21 | 2018 |
| Learning to regrasp by learning to place S Cheng, K Mo, L Shao arXiv preprint arXiv:2109.08817, 2021 | 20 | 2021 |
| OmniHang: Learning to Hang Arbitrary Objects Using Contact Point Correspondences and Neural Collision Estimation Y You, L Shao, T Migimatsu, J Bohg arXiv preprint arXiv:2103.14283, 2021 | 20 | 2021 |
| Diff-lfd: Contact-aware model-based learning from visual demonstration for robotic manipulation via differentiable physics-based simulation and rendering X Zhu, JH Ke, Z Xu, Z Sun, B Bai, J Lv, Q Liu, Y Zeng, Q Ye, C Lu, ... Conference on Robot Learning, 499-512, 2023 | 18 | 2023 |
| Roboassembly: Learning generalizable furniture assembly policy in a novel multi-robot contact-rich simulation environment M Yu, L Shao, Z Chen, T Wu, Q Fan, K Mo, H Dong arXiv preprint arXiv:2112.10143, 2021 | 18 | 2021 |
| DexRepNet: Learning dexterous robotic grasping network with geometric and spatial hand-object representations Q Liu, Y Cui, Q Ye, Z Sun, H Li, G Li, L Shao, J Chen 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2023 | 15 | 2023 |
| Sam-rl: Sensing-aware model-based reinforcement learning via differentiable physics-based simulation and rendering J Lv, Y Feng, C Zhang, S Zhao, L Shao, C Lu The International Journal of Robotics Research, 02783649241284653, 2023 | 14 | 2023 |
| Category-level multi-part multi-joint 3d shape assembly Y Li, K Mo, Y Duan, H Wang, J Zhang, L Shao Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2024 | 12 | 2024 |
| Generalizable long-horizon manipulations with large language models H Zhou, M Ding, W Peng, M Tomizuka, L Shao, C Gan arXiv preprint arXiv:2310.02264, 2023 | 12 | 2023 |
| Clothesnet: An information-rich 3d garment model repository with simulated clothes environment B Zhou, H Zhou, T Liang, Q Yu, S Zhao, Y Zeng, J Lv, S Luo, Q Wang, ... Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 12 | 2023 |
Jeannette BohgAssistant Professor, Stanford UniversityEmail verificata su stanford.edu