| Voyager: An open-ended embodied agent with large language models G Wang, Y Xie, Y Jiang, A Mandlekar, C Xiao, Y Zhu, L Fan, ... arXiv preprint arXiv:2305.16291, 2023 | 428 | 2023 |
| robosuite: A modular simulation framework and benchmark for robot learning Y Zhu, J Wong, A Mandlekar, R Martín-Martín, A Joshi, S Nasiriany, Y Zhu arXiv preprint arXiv:2009.12293, 2020 | 328 | 2020 |
| What matters in learning from offline human demonstrations for robot manipulation A Mandlekar, D Xu, J Wong, S Nasiriany, C Wang, R Kulkarni, L Fei-Fei, ... arXiv preprint arXiv:2108.03298, 2021 | 258 | 2021 |
| Minedojo: Building open-ended embodied agents with internet-scale knowledge L Fan, G Wang, Y Jiang, A Mandlekar, Y Yang, H Zhu, A Tang, DA Huang, ... Advances in Neural Information Processing Systems 35, 18343-18362, 2022 | 250 | 2022 |
| Roboturk: A crowdsourcing platform for robotic skill learning through imitation A Mandlekar, Y Zhu, A Garg, J Booher, M Spero, A Tung, J Gao, ... Conference on Robot Learning, 879-893, 2018 | 230 | 2018 |
| Adversarially robust policy learning: Active construction of physically-plausible perturbations A Mandlekar, Y Zhu, A Garg, L Fei-Fei, S Savarese 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2017 | 193 | 2017 |
| Iris: Implicit reinforcement without interaction at scale for learning control from offline robot manipulation data A Mandlekar, F Ramos, B Boots, S Savarese, L Fei-Fei, A Garg, D Fox 2020 IEEE International Conference on Robotics and Automation (ICRA), 4414-4420, 2020 | 114 | 2020 |
| S4rl: Surprisingly simple self-supervision for offline reinforcement learning in robotics S Sinha, A Mandlekar, A Garg Conference on Robot Learning, 907-917, 2022 | 103 | 2022 |
| Learning to generalize across long-horizon tasks from human demonstrations A Mandlekar, D Xu, R Martín-Martín, S Savarese, L Fei-Fei arXiv preprint arXiv:2003.06085, 2020 | 103 | 2020 |
| Risk-sensitive Inverse Reinforcement Learning via Coherent Risk Models. A Majumdar, S Singh, A Mandlekar, M Pavone Robotics: science and systems 16, 117, 2017 | 78 | 2017 |
| Controlling assistive robots with learned latent actions DP Losey, K Srinivasan, A Mandlekar, A Garg, D Sadigh 2020 IEEE International Conference on Robotics and Automation (ICRA), 378-384, 2020 | 76 | 2020 |
| Deep affordance foresight: Planning through what can be done in the future D Xu, A Mandlekar, R Martín-Martín, Y Zhu, S Savarese, L Fei-Fei 2021 IEEE international conference on robotics and automation (ICRA), 6206-6213, 2021 | 74 | 2021 |
| Scaling robot supervision to hundreds of hours with roboturk: Robotic manipulation dataset through human reasoning and dexterity A Mandlekar, J Booher, M Spero, A Tung, A Gupta, Y Zhu, A Garg, ... 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2019 | 74 | 2019 |
| Human-in-the-loop imitation learning using remote teleoperation A Mandlekar, D Xu, R Martín-Martín, Y Zhu, L Fei-Fei, S Savarese arXiv preprint arXiv:2012.06733, 2020 | 57 | 2020 |
| Ac-teach: A bayesian actor-critic method for policy learning with an ensemble of suboptimal teachers A Kurenkov, A Mandlekar, R Martin-Martin, S Savarese, A Garg arXiv preprint arXiv:1909.04121, 2019 | 55 | 2019 |
| Learning latent actions to control assistive robots DP Losey, HJ Jeon, M Li, K Srinivasan, A Mandlekar, A Garg, J Bohg, ... Autonomous robots 46 (1), 115-147, 2022 | 49 | 2022 |
| Learning multi-arm manipulation through collaborative teleoperation A Tung, J Wong, A Mandlekar, R Martín-Martín, Y Zhu, L Fei-Fei, ... 2021 IEEE International Conference on Robotics and Automation (ICRA), 9212-9219, 2021 | 39 | 2021 |
| Error-aware imitation learning from teleoperation data for mobile manipulation J Wong, A Tung, A Kurenkov, A Mandlekar, L Fei-Fei, S Savarese, ... Conference on Robot Learning, 1367-1378, 2022 | 33 | 2022 |
| Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments M Mittal, C Yu, Q Yu, J Liu, N Rudin, D Hoeller, JL Yuan, R Singh, Y Guo, ... IEEE Robotics and Automation Letters, 2023 | 29 | 2023 |
| Mocoda: Model-based counterfactual data augmentation S Pitis, E Creager, A Mandlekar, A Garg Advances in Neural Information Processing Systems 35, 18143-18156, 2022 | 23 | 2022 |
