| Do as i can, not as i say: Grounding language in robotic affordances M Ahn, A Brohan, N Brown, Y Chebotar, O Cortes, B David, C Finn, C Fu, ... arXiv preprint arXiv:2204.01691, 2022 | 865 | 2022 |
| Population based augmentation: Efficient learning of augmentation policy schedules D Ho, E Liang, X Chen, I Stoica, P Abbeel Proceedings of the 36th International Conference on Machine Learning 97 …, 2019 | 448 | 2019 |
| Do as i can, not as i say: Grounding language in robotic affordances A Brohan, Y Chebotar, C Finn, K Hausman, A Herzog, D Ho, J Ibarz, ... Conference on robot learning, 287-318, 2023 | 232 | 2023 |
| Improvements to context based self-supervised learning TN Mundhenk, D Ho, BY Chen Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 140 | 2018 |
| Retinagan: An object-aware approach to sim-to-real transfer D Ho, K Rao, Z Xu, E Jang, M Khansari, Y Bai International Conference on Robotics and Automation, 2021 | 75 | 2021 |
| Simgan: Hybrid simulator identification for domain adaptation via adversarial reinforcement learning Y Jiang, T Zhang, D Ho, Y Bai, CK Liu, S Levine, J Tan 2021 IEEE International Conference on Robotics and Automation (ICRA), 2884-2890, 2021 | 53 | 2021 |
| Deep rl at scale: Sorting waste in office buildings with a fleet of mobile manipulators A Herzog, K Rao, K Hausman, Y Lu, P Wohlhart, M Yan, J Lin, MG Arenas, ... arXiv preprint arXiv:2305.03270, 2023 | 14 | 2023 |
| Cocoi: contact-aware online context inference for generalizable non-planar pushing Z Xu, W Yu, A Herzog, W Lu, C Fu, M Tomizuka, Y Bai, CK Liu, D Ho 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2021 | 13 | 2021 |
| 1000x faster data augmentation D Ho, E Liang, R Liaw Berkeley Artificial Intelligence Research, 2019 | 12 | 2019 |
| Population based augmentation: efficient learning of augmentation policy schedules 2019 D Ho, E Liang, I Stoica, P Abbeel, X Chen arXiv preprint arXiv:1905.05393, 2021 | 6 | 2021 |
| Bayesian imitation learning for end-to-end mobile manipulation Y Du, D Ho, A Alemi, E Jang, M Khansari International Conference on Machine Learning, 5531-5546, 2022 | 5 | 2022 |
| Practical imitation learning in the real world via task consistency loss M Khansari, D Ho, Y Du, A Fuentes, M Bennice, N Sievers, S Kirmani, ... arXiv preprint arXiv:2202.01862, 2022 | 5 | 2022 |
| Asking for help: Failure prediction in behavioral cloning through value approximation C Gokmen, D Ho, M Khansari 2023 IEEE International Conference on Robotics and Automation (ICRA), 5821-5828, 2023 | 2 | 2023 |
| Implementation of a 3-D laser imager-based robot navigation system with location identification ST Boltinghouse, J Burke, D Ho Mobile Robots V 1388, 14-29, 1991 | 2 | 1991 |
| Practical Visual Deep Imitation Learning via Task-Level Domain Consistency M Khansari, D Ho, Y Du, A Fuentes, M Bennice, N Sievers, S Kirmani, ... 2023 IEEE International Conference on Robotics and Automation (ICRA), 1837-1844, 2023 | 1 | 2023 |
| Universal Controllers with Differentiable Physics for Online System Identification M Guo, W Yu, D Ho, J Wu, Y Bai, K Liu, W Lu | 1 | 2021 |
| Using embeddings, generated using robot action models, in controlling robot to perform robotic task D Ho, E Jang, M Khansari, YQ Du, AA Alemi US Patent App. 18/102,053, 2024 | | 2024 |
| Utilizing past contact physics in robotic manipulation (eg, pushing) of an object Z Xu, W Yu, A Herzog, LU Wenlong, FU Chuyuan, Y Bai, CK Liu, D Ho US Patent 11,833,661, 2023 | | 2023 |
| Mitigating reality gap through feature-level domain adaptation in training of vision-based robot action model M Khansari, D Ho, E Jang, YQ Du US Patent App. 17/986,428, 2023 | | 2023 |
| Scalable Multi-Sensor Robot Imitation Learning via Task-Level Domain Consistency A Fuentes, D Ho, EV Jang, M Bennice, M Khansari, N Sievers, S Kirmani, ... | | 2023 |
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