| 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 | 820 | 2017 |
| Policy shaping: Integrating human feedback with reinforcement learning S Griffith, K Subramanian, J Scholz, CL Isbell, AL Thomaz Advances in neural information processing systems 26, 2013 | 522 | 2013 |
| Overlapping and non-overlapping brain regions for theory of mind and self reflection in individual subjects R Saxe, JM Moran, J Scholz, J Gabrieli Social cognitive and affective neuroscience 1 (3), 229-234, 2006 | 359 | 2006 |
| Brain regions for perceiving and reasoning about other people in school‐aged children RR Saxe, S Whitfield‐Gabrieli, J Scholz, KA Pelphrey Child development 80 (4), 1197-1209, 2009 | 296 | 2009 |
| Distinct regions of right temporo-parietal junction are selective for theory of mind and exogenous attention J Scholz, C Triantafyllou, S Whitfield-Gabrieli, EN Brown, R Saxe PloS one 4 (3), e4869, 2009 | 280 | 2009 |
| 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 | 142 | 2019 |
| The influence of prior record on moral judgment D Kliemann, L Young, J Scholz, R Saxe Neuropsychologia 46 (12), 2949-2957, 2008 | 130 | 2008 |
| 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 | 122 | 2019 |
| Neural evidence for “intuitive prosecution”: The use of mental state information for negative moral verdicts L Young, J Scholz, R Saxe Social neuroscience 6 (3), 302-315, 2011 | 96 | 2011 |
| Cart pushing with a mobile manipulation system: Towards navigation with moveable objects J Scholz, S Chitta, B Marthi, M Likhachev 2011 IEEE International Conference on Robotics and Automation, 6115-6120, 2011 | 94 | 2011 |
| Offline meta-reinforcement learning for industrial insertion TZ Zhao, J Luo, O Sushkov, R Pevceviciute, N Heess, J Scholz, S Schaal, ... 2022 international conference on robotics and automation (ICRA), 6386-6393, 2022 | 80 | 2022 |
| A physics-based model prior for object-oriented mdps J Scholz, M Levihn, C Isbell, D Wingate International Conference on Machine Learning, 1089-1097, 2014 | 76 | 2014 |
| Pves: Position-velocity encoders for unsupervised learning of structured state representations R Jonschkowski, R Hafner, J Scholz, M Riedmiller arXiv preprint arXiv:1705.09805, 2017 | 75 | 2017 |
| Robocat: A self-improving foundation agent for robotic manipulation K Bousmalis, G Vezzani, D Rao, C Devin, AX Lee, M Bauza, T Davchev, ... arXiv preprint arXiv:2306.11706, 2023 | 69 | 2023 |
| Combining motion planning and optimization for flexible robot manipulation J Scholz, M Stilman 2010 10th IEEE-RAS International Conference on Humanoid Robots, 80-85, 2010 | 65 | 2010 |
| 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 | 63 | 2021 |
| Hierarchical decision theoretic planning for navigation among movable obstacles M Levihn, J Scholz, M Stilman Algorithmic Foundations of Robotics X: Proceedings of the Tenth Workshop on …, 2013 | 48 | 2013 |
| S3k: Self-supervised semantic keypoints for robotic manipulation via multi-view consistency M Vecerik, JB Regli, O Sushkov, D Barker, R Pevceviciute, T Rothörl, ... Conference on Robot Learning, 449-460, 2021 | 42 | 2021 |
| Generative predecessor models for sample-efficient imitation learning Y Schroecker, M Vecerik, J Scholz arXiv preprint arXiv:1904.01139, 2019 | 38 | 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 |
Mel VecerikDeepMind, University College LondonDirección de correo verificada de ucl.ac.uk
