| Automatic goal generation for reinforcement learning agents C Florensa, D Held, X Geng, P Abbeel International Conference on Machine Learning (ICML), 2017 | 580* | 2017 |
| Reverse curriculum generation for reinforcement learning C Florensa, D Held, M Wulfmeier, P Abbeel Conference on Robot Learning (CoRL), 2017 | 531 | 2017 |
| Stochastic neural networks for hierarchical reinforcement learning C Florensa, Y Duan, P Abbeel International Conference on Learning Representations (ICLR), 2017 | 436 | 2017 |
| Goal-conditioned Imitation Learning Y Ding, C Florensa, M Phielipp, P Abbeel Advances in Neural Information Processing Systems (NeurIPS), 2019 | 266 | 2019 |
| Sub-policy Adaptation for Hierarchical Reinforcement Learning AC Li, C Florensa, I Clavera, P Abbeel arXiv preprint arXiv:1906.05862, 2019 | 99 | 2019 |
| Guided uncertainty-aware policy optimization: Combining learning and model-based strategies for sample-efficient policy learning MA Lee, C Florensa, J Tremblay, N Ratliff, A Garg, F Ramos, D Fox 2020 IEEE international conference on robotics and automation (ICRA), 7505-7512, 2020 | 70 | 2020 |
| Self-supervised learning of image embedding for continuous control C Florensa, J Degrave, N Heess, JT Springenberg, M Riedmiller arXiv preprint arXiv:1901.00943, 2019 | 55 | 2019 |
| Which mutual-information representation learning objectives are sufficient for control? K Rakelly, A Gupta, C Florensa, S Levine Advances in Neural Information Processing Systems 34, 26345-26357, 2021 | 34 | 2021 |
| Capacity planning with competitive decision-makers: Trilevel MILP formulation, degeneracy, and solution approaches C Florensa, P Garcia-Herreros, P Misra, E Arslan, S Mehta, IE Grossmann European Journal of Operational Research 262 (2), 449-463, 2017 | 34 | 2017 |
| Systems and methods for robotic picking Y Duan, X Chen, M Rohaninejad, N Mishra, YX Liu, AA Vaziri, T Haoran, ... US Patent App. 17/014,545, 2021 | 12 | 2021 |
| Guided uncertainty-aware policy optimization: combining model-free and model-based strategies for sample-efficient learning J Tremblay, D Fox, M Lee, C Florensa, ND Ratliff, G Animesh, FT Ramos US Patent 12,109,701, 2024 | 9 | 2024 |
| Adaptive Variance for Changing Sparse-Reward Environments X Lin, P Guo, C Florensa, D Held International Conference on Robotics and Automation, 2019 | 5 | 2019 |
| “The magic of light!”-An entertaining optics and photonics awareness program C Florensa, M Martí, SC Kumar, S Carrasco Education and Training in Optics and Photonics, EWF2, 2013 | 5 | 2013 |
| Systems and methods for robotic picking and perturbation Y Duan, I Rust, AA Vaziri, X Chen, C Florensa US Patent 11,911,903, 2024 | 4 | 2024 |
| Systems and methods for robotic picking and perturbation Y Duan, I Rust, AA Vaziri, X Chen, C Florensa US Patent App. 18/421,266, 2024 | | 2024 |
| What Supervision Scales? Practical Learning Through Interaction CF Campo University of California, Berkeley, 2020 | | 2020 |
