| Evaluating large language models trained on code M Chen, J Tworek, H Jun, Q Yuan, HPO Pinto, J Kaplan, H Edwards, ... arXiv preprint arXiv:2107.03374, 2021 | 4262 | 2021 |
| Training verifiers to solve math word problems K Cobbe, V Kosaraju, M Bavarian, M Chen, H Jun, L Kaiser, M Plappert, ... arXiv preprint arXiv:2110.14168, 2021 | 3342 | 2021 |
| Learning dexterous in-hand manipulation OAIM Andrychowicz, B Baker, M Chociej, R Jozefowicz, B McGrew, ... The International Journal of Robotics Research 39 (1), 3-20, 2020 | 1965 | 2020 |
| Solving Rubik's Cube with a Robot Hand I Akkaya, M Andrychowicz, M Chociej, M Litwin, B McGrew, A Petron, ... arXiv preprint arXiv:1910.07113, 2019 | 1320 | 2019 |
| OpenAI Baselines P Dhariwal, C Hesse, M Plappert, A Radford, J Schulman, S Sidor, Y Wu | 1119 | 2017 |
| Stable baselines A Hill, A Raffin, M Ernestus, A Gleave, A Kanervisto, R Traore, P Dhariwal, ... | 965 | 2018 |
| Parameter Space Noise for Exploration M Plappert, R Houthooft, P Dhariwal, S Sidor, RY Chen, X Chen, T Asfour, ... arXiv preprint arXiv:1706.01905, 2017 | 798 | 2017 |
| Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research M Plappert, M Andrychowicz, A Ray, B McGrew, B Baker, G Powell, ... arXiv preprint arXiv:1802.09464, 2018 | 647 | 2018 |
| The KIT Motion-Language Dataset M Plappert, C Mandery, T Asfour Big Data 4 (4), 236-252, 2016 | 292 | 2016 |
| Learning a bidirectional mapping between human whole-body motion and natural language using deep recurrent neural networks M Plappert, C Mandery, T Asfour Robotics and Autonomous Systems, 2018 | 155 | 2018 |
| Keras-RL M Plappert https://github.com/keras-rl/keras-rl, 2016 | 85 | 2016 |
| Asymmetric self-play for automatic goal discovery in robotic manipulation OAI OpenAI, M Plappert, R Sampedro, T Xu, I Akkaya, V Kosaraju, ... arXiv preprint arXiv:2101.04882, 2021 | 77 | 2021 |
| Introducing chatgpt J Schulman, B Zoph, C Kim, J Hilton, J Menick, J Weng, JFC Uribe, ... OpenAI blog, 2022 | 50 | 2022 |
| Using tactile sensing to improve the sample efficiency and performance of deep deterministic policy gradients for simulated in-hand manipulation tasks A Melnik, L Lach, M Plappert, T Korthals, R Haschke, H Ritter Frontiers in Robotics and AI 8, 538773, 2021 | 41 | 2021 |
| Tactile sensing and deep reinforcement learning for in-hand manipulation tasks A Melnik, L Lach, M Plappert, T Korthals, R Haschke, H Ritter IROS workshop on autonomous object manipulation 34268337, 2019 | 27 | 2019 |
| Dimensionality Reduction for Whole-Body Human Motion Recognition C Mandery, M Plappert, J Borras, T Asfour 19th International Conference on Information Fusion (FUSION), 355-362, 2016 | 21 | 2016 |
| Classification of Human Whole-Body Motion using Hidden Markov Models M Plappert arXiv preprint arXiv:1605.01569, 2016 | 4 | 2016 |
| Predicting Sim-to-Real Transfer with Probabilistic Dynamics Models LM Zhang, M Plappert, W Zaremba arXiv preprint arXiv:2009.12864, 2020 | 3 | 2020 |
| OpenAI 发布训练实体机器人的最新模拟环境 M PLAPPERT, M ANDRYCHOWICZ, A RAY, BOB MCGREW, B BAKER, ... 机器人产业, 2018 | | 2018 |
John SchulmanAnthropicVerified email at anthropic.com
