Combining motion planning and optimization for flexible robot manipulation. - Jonathan Scholz; Mike Stilman (Humanoids, 2010)

Cart pushing with a mobile manipulation system: Towards navigation with moveable objects. - Jonathan Scholz; Sachin Chitta; Bhaskara Marthi; Maxim Likhachev (ICRA, 2011)

Hierarchical Decision Theoretic Planning for Navigation Among Movable Obstacles. - Martin Levihn; Jonathan Scholz; Mike Stilman (WAFR, 2012)

Planning with movable obstacles in continuous environments with uncertain dynamics. - Martin Levihn; Jonathan Scholz; Mike Stilman (ICRA, 2013)

Policy Shaping: Integrating Human Feedback with Reinforcement Learning. - Shane Griffith; Kaushik Subramanian; Jonathan Scholz; Charles L. Isbell Jr.; Andrea Lockerd Thomaz (NIPS, 2013)

A Physics-Based Model Prior for Object-Oriented MDPs. - Jonathan Scholz; Martin Levihn; Charles Lee Isbell Jr.; David Wingate (ICML, 2014)

Learning non-holonomic object models for mobile manipulation. - Jonathan Scholz; Martin Levihn; Charles L. Isbell Jr.; Henrik I. Christensen; Mike Stilman (ICRA, 2015)

Navigation Among Movable Obstacles with learned dynamic constraints. - Jonathan Scholz; Nehchal Jindal; Martin Levihn; Charles L. Isbell Jr.; Henrik I. Christensen (IROS, 2016)

Physics-based reinforcement learning for autonomous manipulation. - Jonathan Scholz (2016)

PVEs: Position-Velocity Encoders for Unsupervised Learning of Structured State Representations. - Rico Jonschkowski; Roland Hafner; Jonathan Scholz; Martin A. Riedmiller (CoRR, 2017)

Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards. - Matej Vecerík; Todd Hester; Jonathan Scholz; Fumin Wang; Olivier Pietquin; Bilal Piot; Nicolas Heess; Thomas Rothörl; Thomas Lampe; Martin A. Riedmiller (CoRR, 2017)

Generative predecessor models for sample-efficient imitation learning. - Yannick Schroecker; Mel Vecerík; Jonathan Scholz (ICLR (Poster), 2019)

A Practical Approach to Insertion with Variable Socket Position Using Deep Reinforcement Learning. - Mel Vecerík; Oleg Sushkov; David Barker; Thomas Rothörl; Todd Hester; Jonathan Scholz (ICRA, 2019)

Improved Exploration through Latent Trajectory Optimization in Deep Deterministic Policy Gradient. - Kevin Sebastian Luck; Mel Vecerík; Simon Stepputtis; Heni Ben Amor; Jonathan Scholz (IROS, 2019)

A Framework for Data-Driven Robotics. - Serkan Cabi; Sergio Gómez Colmenarejo; Alexander Novikov 001; Ksenia Konyushkova; Scott E. Reed; Rae Jeong; Konrad Zolna; Yusuf Aytar; David Budden; Mel Vecerík; Oleg Sushkov; David Barker; et al. (CoRR, 2019)

S3K: Self-Supervised Semantic Keypoints for Robotic Manipulation via Multi-View Consistency. - Mel Vecerík; Jean-Baptiste Regli; Oleg Sushkov; David Barker; Rugile Pevceviciute; Thomas Rothörl; Raia Hadsell; Lourdes Agapito; Jonathan Scholz (CoRL, 2020)

Scaling data-driven robotics with reward sketching and batch reinforcement learning. - Serkan Cabi; Sergio Gómez Colmenarejo; Alexander Novikov 001; Ksenia Konyushkova; Scott E. Reed; Rae Jeong; Konrad Zolna; Yusuf Aytar; David Budden; Mel Vecerík; Oleg Sushkov; David Barker; et al. (Robotics: Science and Systems, 2020)

S3K: Self-Supervised Semantic Keypoints for Robotic Manipulation via Multi-View Consistency. - Mel Vecerík; Jean-Baptiste Regli; Oleg Sushkov; David Barker; Rugile Pevceviciute; Thomas Rothörl; Christopher Schuster; Raia Hadsell; Lourdes Agapito; Jonathan Scholz (CoRR, 2020)

Robust Multi-Modal Policies for Industrial Assembly via Reinforcement Learning and Demonstrations: A Large-Scale Study. - Jianlan Luo; Oleg Sushkov; Rugile Pevceviciute; Wenzhao Lian; Chang Su; Mel Vecerík; Ning Ye; Stefan Schaal; Jonathan Scholz (Robotics: Science and Systems, 2021)

Wish you were here: Hindsight Goal Selection for long-horizon dexterous manipulation. - Todor Davchev; Oleg Olegovich Sushkov; Jean-Baptiste Regli; Stefan Schaal; Yusuf Aytar; Markus Wulfmeier; Jon Scholz (ICLR, 2022)

Few-Shot Keypoint Detection as Task Adaptation via Latent Embeddings. - Mel Vecerík; Jackie Kay; Raia Hadsell; Lourdes Agapito; Jon Scholz (ICRA, 2022)

Offline Meta-Reinforcement Learning for Industrial Insertion. - Tony Z. Zhao; Jianlan Luo; Oleg Sushkov; Rugile Pevceviciute; Nicolas Heess; Jon Scholz; Stefan Schaal; Sergey Levine (ICRA, 2022)

Lossless Adaptation of Pretrained Vision Models For Robotic Manipulation. - Mohit Sharma 001; Claudio Fantacci; Yuxiang Zhou; Skanda Koppula; Nicolas Heess; Jon Scholz; Yusuf Aytar (ICLR, 2023)

RoboCat: A Self-Improving Foundation Agent for Robotic Manipulation. - Konstantinos Bousmalis; Giulia Vezzani; Dushyant Rao; Coline Devin; Alex X. Lee; Maria Bauzá 001; Todor Davchev; Yuxiang Zhou; Agrim Gupta; Akhil Raju; Antoine Laurens; Claudio Fantacci; et al. (CoRR, 2023)

Deep SE(3)-Equivariant Geometric Reasoning for Precise Placement Tasks. - Ben Eisner; Yi Yang 007; Todor Davchev; Mel Vecerík; Jonathan Scholz; David Held (ICLR, 2024)

RoboTAP: Tracking Arbitrary Points for Few-Shot Visual Imitation. - Mel Vecerík; Carl Doersch; Yi Yang 007; Todor Davchev; Yusuf Aytar; Guangyao Zhou; Raia Hadsell; Lourdes Agapito; Jon Scholz (ICRA, 2024)

DemoStart: Demonstration-led auto-curriculum applied to sim-to-real with multi-fingered robots. - Maria Bauzá 001; 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; et al. (CoRR, 2024)

RoboCat: A Self-Improving Generalist Agent for Robotic Manipulation. - 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; et al. (Trans. Mach. Learn. Res., 2024)

RoboBallet: Planning for multirobot reaching with graph neural networks and reinforcement learning. - Matthew Lai; Keegan Go; Zhibin Li 001; Torsten Kröger; Stefan Schaal; Kelsey Allen; Jonathan Scholz (Sci. Robotics, 2025)