| Deep q-learning from demonstrations T Hester, M Vecerik, O Pietquin, M Lanctot, T Schaul, B Piot, D Horgan, ... Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 1320 | 2018 |
| Leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards M Vecerik, T Hester, J Scholz, F Wang, O Pietquin, B Piot, N Heess, ... arXiv preprint arXiv:1707.08817, 2017 | 842 | 2017 |
| Sim-to-Real Robot Learning from Pixels with Progressive Nets AR Andrei, V Mel, R Thomas, H Nicolas, P Razvan 1st Conference on Robot Learning, Mountain View, 2017 | 649* | 2017 |
| Safe exploration in continuous action spaces G Dalal, K Dvijotham, M Vecerik, T Hester, C Paduraru, Y Tassa arXiv preprint arXiv:1801.08757, 2018 | 541 | 2018 |
| Learning from demonstrations for real world reinforcement learning T Hester, M Vecerik, O Pietquin, M Lanctot, T Schaul, B Piot, A Sendonaris, ... | 183 | 2017 |
| Scaling data-driven robotics with reward sketching and batch reinforcement learning S Cabi, SG Colmenarejo, A Novikov, K Konyushkova, S Reed, R Jeong, ... arXiv preprint arXiv:1909.12200, 2019 | 146 | 2019 |
| Observe and look further: Achieving consistent performance on atari T Pohlen, B Piot, T Hester, MG Azar, D Horgan, D Budden, G Barth-Maron, ... arXiv preprint arXiv:1805.11593, 2018 | 143 | 2018 |
| A practical approach to insertion with variable socket position using deep reinforcement learning M Vecerik, O Sushkov, D Barker, T Rothörl, T Hester, J Scholz 2019 International Conference on Robotics and Automation (ICRA), 754-760, 2019 | 126 | 2019 |
| TAPIR: Tracking Any Point with per-frame Initialization and temporal Refinement C Doersch, Y Yang, M Vecerik, D Gokay, A Gupta, Y Aytar, J Carreira, ... arXiv preprint arXiv:2306.08637, 2023 | 98 | 2023 |
| Robust Multi-Modal Policies for Industrial Assembly via Reinforcement Learning and Demonstrations: A Large-Scale Study J Luo, O Sushkov, R Pevceviciute, W Lian, C Su, M Vecerik, N Ye, ... arXiv preprint arXiv:2103.11512, 2021 | 69 | 2021 |
| S3K: Self-Supervised Semantic Keypoints for Robotic Manipulation via Multi-View Consistency M Vecerik, JB Regli, O Sushkov, D Barker, R Pevceviciute, T Rothörl, ... Proceedings of the 2020 Conference on Robot Learning 155, 449--460, 2020 | 44 | 2020 |
| Generative predecessor models for sample-efficient imitation learning Y Schroecker, M Vecerik, J Scholz arXiv preprint arXiv:1904.01139, 2019 | 39 | 2019 |
| A Framework for Data-Driven Robotics S Cabi, SG Colmenarejo, A Novikov, K Konyushkova, S Reed, R Jeong, ... arXiv preprint arXiv:1909.12200, 2019 | 27 | 2019 |
| RoboTAP: Tracking Arbitrary Points for Few-Shot Visual Imitation M Vecerik, C Doersch, Y Yang, T Davchev, Y Aytar, G Zhou, R Hadsell, ... arXiv preprint arXiv:2308.15975, 2023 | 26 | 2023 |
| Data-efficient reinforcement learning for continuous control tasks M Riedmiller, R Hafner, M Vecerik, TP Lillicrap, T Lampe, I Popov, ... US Patent 10,664,725, 2020 | 15 | 2020 |
| Imitation learning using a generative predecessor neural network M Vecerik, Y Schroecker, JK Scholz US Patent 10,872,294, 2020 | 10 | 2020 |
| Improved exploration through latent trajectory optimization in deep deterministic policy gradient KS Luck, M Vecerik, S Stepputtis, HB Amor, J Scholz 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2019 | 10 | 2019 |
| Leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards. 2017 M Vecerik, T Hester, J Scholz, F Wang, O Pietquin, B Piot, N Heess, ... URL: http://arxiv. org/abs/1707.08817, 0 | 4 | |
| Few-Shot Keypoint Detection as Task Adaptation via Latent Embeddings M Vecerik, J Kay, R Hadsell, L Agapito, J Scholz 2022 International Conference on Robotics and Automation (ICRA), 1251-1257, 2022 | 3 | 2022 |
| Neural networks for selecting actions to be performed by a robotic agent R Pascanu, RT Hadsell, M Vecerik, T Rothoerl, AA Rusu, NMO Heess US Patent 10,632,618, 2020 | 2 | 2020 |
