| Isaac gym: High performance gpu-based physics simulation for robot learning V Makoviychuk, L Wawrzyniak, Y Guo, M Lu, K Storey, M Macklin, ... arXiv preprint arXiv:2108.10470, 2021 | 1084* | 2021 |
| Closing the sim-to-real loop: Adapting simulation randomization with real world experience Y Chebotar, A Handa, V Makoviychuk, M Macklin, J Issac, N Ratliff, D Fox 2019 International Conference on Robotics and Automation (ICRA), 8973-8979, 2019 | 651 | 2019 |
| Is independent learning all you need in the starcraft multi-agent challenge? CS De Witt, T Gupta, D Makoviichuk, V Makoviychuk, PHS Torr, M Sun, ... arXiv preprint arXiv:2011.09533, 2020 | 410 | 2020 |
| Gpu-accelerated robotic simulation for distributed reinforcement learning J Liang, V Makoviychuk, A Handa, N Chentanez, M Macklin, D Fox Conference on Robot Learning, 270-282, 2018 | 196 | 2018 |
| Dextreme: Transfer of agile in-hand manipulation from simulation to reality A Handa, A Allshire, V Makoviychuk, A Petrenko, R Singh, J Liu, ... 2023 IEEE International Conference on Robotics and Automation (ICRA), 5977-5984, 2023 | 144 | 2023 |
| Accelerated policy learning with parallel differentiable simulation J Xu, V Makoviychuk, Y Narang, F Ramos, W Matusik, A Garg, M Macklin arXiv preprint arXiv:2204.07137, 2022 | 112 | 2022 |
| Physics-based motion capture imitation with deep reinforcement learning N Chentanez, M Müller, M Macklin, V Makoviychuk, S Jeschke Proceedings of the 11th ACM SIGGRAPH Conference on Motion, Interaction and …, 2018 | 108 | 2018 |
| Non-smooth newton methods for deformable multi-body dynamics M Macklin, K Erleben, M Müller, N Chentanez, S Jeschke, V Makoviychuk ACM Transactions on Graphics (TOG) 38 (5), 1-20, 2019 | 96 | 2019 |
| Transferring dexterous manipulation from gpu simulation to a remote real-world trifinger A Allshire, M MittaI, V Lodaya, V Makoviychuk, D Makoviichuk, F Widmaier, ... 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2022 | 75 | 2022 |
| rl-games: A high-performance framework for reinforcement learning D Makoviichuk, V Makoviychuk Denys88/rl_games, 2021 | 70 | 2021 |
| Learning physically simulated tennis skills from broadcast videos Y Yuan, V Makoviychuk, Y Guo, S Fidler, XB Peng, K Fatahalian ACM Trans. Graph 42 (4), 2023 | 57 | 2023 |
| Envpool: A highly parallel reinforcement learning environment execution engine J Weng, M Lin, S Huang, B Liu, D Makoviichuk, V Makoviychuk, Z Liu, ... Advances in Neural Information Processing Systems 35, 22409-22421, 2022 | 56 | 2022 |
| Tesseract: Tensorised actors for multi-agent reinforcement learning A Mahajan, M Samvelyan, L Mao, V Makoviychuk, A Garg, J Kossaifi, ... International Conference on Machine Learning, 7301-7312, 2021 | 43 | 2021 |
| Gavriel State. 2021. Isaac Gym: High performance GPU-based physics simulation for robot learning V Makoviychuk, L Wawrzyniak, Y Guo, M Lu, K Storey, M Macklin, ... arXiv preprint arXiv:2108.10470, 2021 | 42 | 2021 |
| In-hand object pose tracking via contact feedback and gpu-accelerated robotic simulation J Liang, A Handa, K Van Wyk, V Makoviychuk, O Kroemer, D Fox 2020 IEEE International Conference on Robotics and Automation (ICRA), 6203-6209, 2020 | 37 | 2020 |
| Dexpbt: Scaling up dexterous manipulation for hand-arm systems with population based training A Petrenko, A Allshire, G State, A Handa, V Makoviychuk arXiv preprint arXiv:2305.12127, 2023 | 16 | 2023 |
| OSCAR: Data-driven operational space control for adaptive and robust robot manipulation J Wong, V Makoviychuk, A Anandkumar, Y Zhu 2022 International Conference on Robotics and Automation (ICRA), 10519-10526, 2022 | 14 | 2022 |
| Reinforcement learning in factored action spaces using tensor decompositions A Mahajan, M Samvelyan, L Mao, V Makoviychuk, A Garg, J Kossaifi, ... arXiv preprint arXiv:2110.14538, 2021 | 11 | 2021 |
| Dextrah-g: Pixels-to-action dexterous arm-hand grasping with geometric fabrics TGW Lum, M Matak, V Makoviychuk, A Handa, A Allshire, T Hermans, ... arXiv preprint arXiv:2407.02274, 2024 | 9 | 2024 |
| Open rl benchmark: Comprehensive tracked experiments for reinforcement learning S Huang, Q Gallouédec, F Felten, A Raffin, RFJ Dossa, Y Zhao, ... arXiv preprint arXiv:2402.03046, 2024 | 9 | 2024 |
Ankur HandaPrincipal Scientist, NVIDIABestätigte E-Mail-Adresse bei doc.ic.ac.uk
