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Jonathan Scholz
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2020 – today
- 2024
- [j1]Konstantinos Bousmalis, Giulia Vezzani, Dushyant Rao, Coline Manon Devin, Alex X. Lee, Maria Bauzá Villalonga, Todor Davchev, Yuxiang Zhou, Agrim Gupta, Akhil Raju, Antoine Laurens, Claudio Fantacci, Valentin Dalibard, Martina Zambelli, Murilo Fernandes Martins, Rugile Pevceviciute, Michiel Blokzijl, Misha Denil, Nathan Batchelor, Thomas Lampe, Emilio Parisotto, Konrad Zolna, Scott E. Reed, Sergio Gómez Colmenarejo, Jon Scholz, Abbas Abdolmaleki, Oliver Groth, Jean-Baptiste Regli, Oleg Sushkov, Thomas Rothörl, José Enrique Chen, Yusuf Aytar, Dave Barker, Joy Ortiz, Martin A. Riedmiller, Jost Tobias Springenberg, Raia Hadsell, Francesco Nori, Nicolas Heess:
RoboCat: A Self-Improving Generalist Agent for Robotic Manipulation. Trans. Mach. Learn. Res. 2024 (2024) - [c20]Ben Eisner, Yi Yang, Todor Davchev, Mel Vecerík, Jonathan Scholz, David Held:
Deep SE(3)-Equivariant Geometric Reasoning for Precise Placement Tasks. ICLR 2024 - [c19]Mel Vecerík, Carl Doersch, Yi Yang, Todor Davchev, Yusuf Aytar, Guangyao Zhou, Raia Hadsell, Lourdes Agapito, Jon Scholz:
RoboTAP: Tracking Arbitrary Points for Few-Shot Visual Imitation. ICRA 2024: 5397-5403 - [i16]Ben Eisner, Yi Yang, Todor Davchev, Mel Vecerík, Jonathan Scholz, David Held:
Deep SE(3)-Equivariant Geometric Reasoning for Precise Placement Tasks. CoRR abs/2404.13478 (2024) - [i15]Maria Bauzá, José Enrique Chen, Valentin Dalibard, Nimrod Gileadi, Roland Hafner, Murilo F. Martins, Joss Moore, Rugile Pevceviciute, Antoine Laurens, Dushyant Rao, Martina Zambelli, Martin A. Riedmiller, Jon Scholz, Konstantinos Bousmalis, Francesco Nori, Nicolas Heess:
DemoStart: Demonstration-led auto-curriculum applied to sim-to-real with multi-fingered robots. CoRR abs/2409.06613 (2024) - 2023
- [c18]Mohit Sharma, Claudio Fantacci, Yuxiang Zhou, Skanda Koppula, Nicolas Heess, Jon Scholz, Yusuf Aytar:
Lossless Adaptation of Pretrained Vision Models For Robotic Manipulation. ICLR 2023 - [i14]Mohit Sharma, Claudio Fantacci, Yuxiang Zhou, Skanda Koppula, Nicolas Heess, Jon Scholz, Yusuf Aytar:
Lossless Adaptation of Pretrained Vision Models For Robotic Manipulation. CoRR abs/2304.06600 (2023) - [i13]Konstantinos Bousmalis, Giulia Vezzani, Dushyant Rao, Coline Devin, Alex X. Lee, Maria Bauzá, Todor Davchev, Yuxiang Zhou, Agrim Gupta, Akhil Raju, Antoine Laurens, Claudio Fantacci, Valentin Dalibard, Martina Zambelli, Murilo F. Martins, Rugile Pevceviciute, Michiel Blokzijl, Misha Denil, Nathan Batchelor, Thomas Lampe, Emilio Parisotto, Konrad Zolna, Scott E. Reed, Sergio Gómez Colmenarejo, Jon Scholz, Abbas Abdolmaleki, Oliver Groth, Jean-Baptiste Regli, Oleg Sushkov, Thomas Rothörl, José Enrique Chen, Yusuf Aytar, Dave Barker, Joy Ortiz, Martin A. Riedmiller, Jost Tobias Springenberg, Raia Hadsell, Francesco Nori, Nicolas Heess:
RoboCat: A Self-Improving Foundation Agent for Robotic Manipulation. CoRR abs/2306.11706 (2023) - [i12]Mel Vecerík, Carl Doersch, Yi Yang, Todor Davchev, Yusuf Aytar, Guangyao Zhou, Raia Hadsell, Lourdes Agapito, Jon Scholz:
RoboTAP: Tracking Arbitrary Points for Few-Shot Visual Imitation. CoRR abs/2308.15975 (2023) - 2022
- [c17]Todor Davchev, Oleg Olegovich Sushkov, Jean-Baptiste Regli, Stefan Schaal, Yusuf Aytar, Markus Wulfmeier, Jon Scholz:
Wish you were here: Hindsight Goal Selection for long-horizon dexterous manipulation. ICLR 2022 - [c16]Mel Vecerík, Jackie Kay, Raia Hadsell, Lourdes Agapito
, Jon Scholz:
Few-Shot Keypoint Detection as Task Adaptation via Latent Embeddings. ICRA 2022: 1251-1257 - [c15]Tony Z. Zhao, Jianlan Luo, Oleg Sushkov, Rugile Pevceviciute, Nicolas Heess, Jon Scholz, Stefan Schaal, Sergey Levine:
Offline Meta-Reinforcement Learning for Industrial Insertion. ICRA 2022: 6386-6393 - 2021
- [c14]Jianlan Luo, Oleg Sushkov, Rugile Pevceviciute, Wenzhao Lian, Chang Su, Mel Vecerík, Ning Ye, Stefan Schaal, Jonathan Scholz:
Robust Multi-Modal Policies for Industrial Assembly via Reinforcement Learning and Demonstrations: A Large-Scale Study. Robotics: Science and Systems 2021 - [i11]Jianlan Luo, Oleg Sushkov, Rugile Pevceviciute, Wenzhao Lian, Chang Su, Mel Vecerík, Ning Ye, Stefan Schaal, Jonathan Scholz:
Robust Multi-Modal Policies for Industrial Assembly via Reinforcement Learning and Demonstrations: A Large-Scale Study. CoRR abs/2103.11512 (2021) - [i10]Tony Z. Zhao, Jianlan Luo, Oleg Sushkov, Rugile Pevceviciute, Nicolas Heess, Jonathan Scholz, Stefan Schaal, Sergey Levine:
Offline Meta-Reinforcement Learning for Industrial Insertion. CoRR abs/2110.04276 (2021) - [i9]Todor Davchev, Oleg Sushkov, Jean-Baptiste Regli, Stefan Schaal, Yusuf Aytar, Markus Wulfmeier, Jon Scholz:
Wish you were here: Hindsight Goal Selection for long-horizon dexterous manipulation. CoRR abs/2112.00597 (2021) - [i8]Mel Vecerík, Jackie Kay, Raia Hadsell, Lourdes Agapito, Jon Scholz:
Few-Shot Keypoint Detection as Task Adaptation via Latent Embeddings. CoRR abs/2112.04910 (2021) - 2020
- [c13]Mel Vecerík, Jean-Baptiste Regli, Oleg Sushkov, David Barker, Rugile Pevceviciute, Thomas Rothörl, Raia Hadsell, Lourdes Agapito, Jonathan Scholz:
S3K: Self-Supervised Semantic Keypoints for Robotic Manipulation via Multi-View Consistency. CoRL 2020: 449-460 - [c12]Serkan Cabi, Sergio Gómez Colmenarejo, Alexander Novikov, Ksenia Konyushkova, Scott E. Reed, Rae Jeong, Konrad Zolna, Yusuf Aytar, David Budden, Mel Vecerík, Oleg Sushkov, David Barker, Jonathan Scholz, Misha Denil, Nando de Freitas, Ziyu Wang:
Scaling data-driven robotics with reward sketching and batch reinforcement learning. Robotics: Science and Systems 2020 - [i7]Mel Vecerík, Jean-Baptiste Regli, Oleg Sushkov, David Barker, Rugile Pevceviciute, Thomas Rothörl, Christopher Schuster, Raia Hadsell, Lourdes Agapito, Jonathan Scholz:
S3K: Self-Supervised Semantic Keypoints for Robotic Manipulation via Multi-View Consistency. CoRR abs/2009.14711 (2020)
2010 – 2019
- 2019
- [c11]Yannick Schroecker, Mel Vecerík, Jonathan Scholz:
Generative predecessor models for sample-efficient imitation learning. ICLR (Poster) 2019 - [c10]Mel Vecerík, Oleg Sushkov, David Barker, Thomas Rothörl, Todd Hester, Jonathan Scholz:
A Practical Approach to Insertion with Variable Socket Position Using Deep Reinforcement Learning. ICRA 2019: 754-760 - [c9]Kevin Sebastian Luck
Improved Exploration through Latent Trajectory Optimization in Deep Deterministic Policy Gradient. IROS 2019: 3704-3711 - [i6]Yannick Schroecker, Mel Vecerík, Jonathan Scholz:
Generative predecessor models for sample-efficient imitation learning. CoRR abs/1904.01139 (2019) - [i5]Serkan Cabi, Sergio Gómez Colmenarejo, Alexander Novikov, Ksenia Konyushkova, Scott E. Reed, Rae Jeong, Konrad Zolna, Yusuf Aytar, David Budden, Mel Vecerík, Oleg Sushkov, David Barker, Jonathan Scholz, Misha Denil, Nando de Freitas, Ziyu Wang:
A Framework for Data-Driven Robotics. CoRR abs/1909.12200 (2019) - [i4]Kevin Sebastian Luck, Mel Vecerík, Simon Stepputtis, Heni Ben Amor, Jonathan Scholz:
Improved Exploration through Latent Trajectory Optimization in Deep Deterministic Policy Gradient. CoRR abs/1911.06833 (2019) - 2018
- [i3]Mel Vecerík, Oleg Sushkov, David Barker, Thomas Rothörl, Todd Hester, Jonathan Scholz:
A Practical Approach to Insertion with Variable Socket Position Using Deep Reinforcement Learning. CoRR abs/1810.01531 (2018) - 2017
- [i2]Rico Jonschkowski, Roland Hafner, Jonathan Scholz, Martin A. Riedmiller:
PVEs: Position-Velocity Encoders for Unsupervised Learning of Structured State Representations. CoRR abs/1705.09805 (2017) - [i1]Matej Vecerík, Todd Hester, Jonathan Scholz, Fumin Wang, Olivier Pietquin, Bilal Piot, Nicolas Heess, Thomas Rothörl, Thomas Lampe, Martin A. Riedmiller:
Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards. CoRR abs/1707.08817 (2017) - 2016
- [c8]Jonathan Scholz, Nehchal Jindal, Martin Levihn, Charles L. Isbell Jr., Henrik I. Christensen
Navigation Among Movable Obstacles with learned dynamic constraints. IROS 2016: 3706-3713 - 2015
- [c7]Jonathan Scholz, Martin Levihn, Charles L. Isbell Jr., Henrik I. Christensen
Learning non-holonomic object models for mobile manipulation. ICRA 2015: 5531-5536 - 2014
- [c6]Jonathan Scholz, Martin Levihn, Charles Lee Isbell Jr., David Wingate:
A Physics-Based Model Prior for Object-Oriented MDPs. ICML 2014: 1089-1097 - 2013
- [c5]Martin Levihn, Jonathan Scholz, Mike Stilman:
Planning with movable obstacles in continuous environments with uncertain dynamics. ICRA 2013: 3832-3838 - [c4]Shane Griffith, Kaushik Subramanian, Jonathan Scholz, Charles L. Isbell Jr., Andrea Lockerd Thomaz:
Policy Shaping: Integrating Human Feedback with Reinforcement Learning. NIPS 2013: 2625-2633 - 2012
- [c3]Martin Levihn, Jonathan Scholz, Mike Stilman:
Hierarchical Decision Theoretic Planning for Navigation Among Movable Obstacles. WAFR 2012: 19-35 - 2011
- [c2]Jonathan Scholz, Sachin Chitta, Bhaskara Marthi, Maxim Likhachev:
Cart pushing with a mobile manipulation system: Towards navigation with moveable objects. ICRA 2011: 6115-6120 - 2010
- [c1]Jonathan Scholz, Mike Stilman:
Combining motion planning and optimization for flexible robot manipulation. Humanoids 2010: 80-85
Coauthor Index
aka: Oleg Olegovich Sushkov
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