Jonathan Scholz
Jonathan Scholz
Vahvistettu sähköpostiosoite verkkotunnuksessa
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
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
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
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
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
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
The influence of prior record on moral judgment
D Kliemann, L Young, J Scholz, R Saxe
Neuropsychologia 46 (12), 2949-2957, 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
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
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
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
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
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
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
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
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
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
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
Generative predecessor models for sample-efficient imitation learning
Y Schroecker, M Vecerik, J Scholz
arXiv preprint arXiv:1904.01139, 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
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Artikkelit 1–20