| Open X-Embodiment: Robotic Learning Datasets and RT-X Models A O’Neill, A Rehman, A Maddukuri, A Gupta, A Padalkar, A Lee, A Pooley, ... 2024 IEEE International Conference on Robotics and Automation (ICRA), 6892-6903, 2024 | 474 | 2024 |
| Recurrent autoregressive networks for online multi-object tracking K Fang, Y Xiang, X Li, S Savarese IEEE Winter Conference on Applications of Computer Vision (WACV), 2018 | 329 | 2018 |
| Learning task-oriented grasping for tool manipulation from simulated self-supervision K Fang, Y Zhu, A Garg, A Kurenkov, V Mehta, L Fei-Fei, S Savarese Robotics: Science and Systems (RSS), 2018 | 253 | 2018 |
| Scene memory transformer for embodied agents in long-horizon tasks K Fang, A Toshev, L Fei-Fei, S Savarese IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019 | 228 | 2019 |
| Delay: Robust spatial layout estimation for cluttered indoor scenes S Dasgupta, K Fang, K Chen, S Savarese IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 | 183 | 2016 |
| Synergies between affordance and geometry: 6-dof grasp detection via implicit representations Z Jiang, Y Zhu, M Svetlik, K Fang, Y Zhu arXiv preprint arXiv:2104.01542, 2021 | 160 | 2021 |
| Multi-task domain adaptation for deep learning of instance grasping from simulation K Fang, Y Bai, S Hinterstoisser, S Savarese, M Kalakrishnan IEEE International Conference on Robotics and Automation (ICRA), 2018 | 131 | 2018 |
| Demo2Vec: Reasoning object affordances from online videos K Fang, TL Wu, D Yang, S Savarese, JJ Lim IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 | 124 | 2018 |
| KETO: Learning Keypoint Representations for Tool Manipulation Z Qin, K Fang, Y Zhu, L Fei-Fei, S Savarese IEEE International Conference on Robotics and Automation (ICRA), 2020 | 116 | 2020 |
| Bridgedata v2: A dataset for robot learning at scale HR Walke, K Black, TZ Zhao, Q Vuong, C Zheng, P Hansen-Estruch, ... Conference on Robot Learning, 1723-1736, 2023 | 107 | 2023 |
| Moka: Open-world robotic manipulation through mark-based visual prompting K Fang, F Liu, P Abbeel, S Levine Robotics: Science and Systems (RSS), 2024 | 68* | 2024 |
| Dynamics Learning with Cascaded Variational Inference for Multi-Step Manipulation K Fang, Y Zhu, A Garg, S Savarese, L Fei-Fei Conference on Robot Learning (CoRL), 2019 | 51 | 2019 |
| Machine learning methods and apparatus for robotic manipulation and that utilize multi-task domain adaptation Y Bai, K Fang, S Hinterstoisser, M Kalakrishnan US Patent 10,773,382, 2020 | 43 | 2020 |
| Multi-stage cable routing through hierarchical imitation learning J Luo, C Xu, X Geng, G Feng, K Fang, L Tan, S Schaal, S Levine IEEE Transactions on Robotics, 2024 | 39 | 2024 |
| Adaptive Procedural Task Generation for Hard-Exploration Problems K Fang, Y Zhu, S Savarese, L Fei-Fei International Conference on Learning Representations (ICLR), 2021 | 33 | 2021 |
| Planning to practice: Efficient online fine-tuning by composing goals in latent space K Fang, P Yin, A Nair, S Levine 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2022 | 30 | 2022 |
| Goal representations for instruction following: A semi-supervised language interface to control V Myers, AW He, K Fang, HR Walke, P Hansen-Estruch, CA Cheng, ... Conference on Robot Learning, 3894-3908, 2023 | 27 | 2023 |
| Generalization with lossy affordances: Leveraging broad offline data for learning visuomotor tasks K Fang, P Yin, A Nair, HR Walke, G Yan, S Levine Conference on Robot Learning, 106-117, 2023 | 25 | 2023 |
| Discovering Generalizable Skills via Automated Generation of Diverse Tasks K Fang, Y Zhu, S Savarese, L Fei-Fei Robotics: Science and Systems (RSS), 2021 | 13 | 2021 |
| Active Task Randomization: Learning Robust Skills via Unsupervised Generation of Diverse and Feasible Tasks K Fang, T Migimatsu, A Mandlekar, L Fei-Fei, J Bohg 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2023 | 11* | 2023 |
Silvio SavareseAssociate Professor of Computer Science at Stanford UniversityΗ διεύθυνση ηλεκτρονικού ταχυδρομείου έχει επαληθευτεί στον τομέα stanford.edu
