| Harnessing Reinforcement Learning for Neural Motion Planning T Jurgenson, A Tamar Robotics: Science and Systems 2019, 2019 | 68 | 2019 |
| Sub-Goal Trees – a Framework for Goal-Based Reinforcement Learning T Jurgenson, O Avner, E Groshev, A Tamar International Conference on Machine Learning (ICML) 2020, 2020 | 40 | 2020 |
| Sub-Goal Trees--a Framework for Goal-Directed Trajectory Prediction and Optimization T Jurgenson, E Groshev, A Tamar arXiv preprint arXiv:1906.05329, 2019 | 12 | 2019 |
| Mamba: an effective world model approach for meta-reinforcement learning Z Rimon, T Jurgenson, O Krupnik, G Adler, A Tamar arXiv preprint arXiv:2403.09859, 2024 | 4 | 2024 |
| Goal-Conditioned Supervised Learning with Sub-Goal Prediction T Jurgenson, A Tamar arXiv preprint arXiv:2305.10171, 2023 | 2 | 2023 |
| Learning decision trees with stochastic linear classifiers T Jurgenson, Y Mansour Algorithmic Learning Theory, 489-528, 2018 | 2 | 2018 |
| Fine-tuning generative models as an inference method for robotic tasks O Krupnik, E Shafer, T Jurgenson, A Tamar Conference on Robot Learning, 866-886, 2023 | 1 | 2023 |
| Hierarchical Planning for Rope Manipulation using Knot Theory and a Learned Inverse Model M Sudry, T Jurgenson, A Tamar, E Karpas 7th Annual Conference on Robot Learning, 2023 | 1 | 2023 |
| RoboArm-NMP: a Learning Environment for Neural Motion Planning T Jurgenson, M Sudry, G Avineri, A Tamar arXiv preprint arXiv:2405.16335, 2024 | | 2024 |
Aviv TamarTechnion在 technion.ac.il 的电子邮件经过验证
