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Jan Schneider

Empirical Inference Doctoral Researcher

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Office: N 2.017 Max-Planck-Ring 4 72076 Tübingen [email protected]
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I am an ELLIS PhD student supervised by Dieter Büchler and Bernhard Schölkopf at the Max Planck Institute for Intelligent Systems and Ingmar Posner at the University of Oxford. My research interests generally lie in reinforcement learning and robotics. More concretely, I am interested in the role of action representations in reinforcement learning, discovering and exploiting structure in the learning process, and applying reinforcement learning to muscular robots for solving dynamic tasks.

For more information on my research projects, check out my Google Scholar.

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Empirical Inference Conference Paper RP1M: A Large-Scale Motion Dataset for Piano Playing with Bi-Manual Dexterous Robot Hands Zhao*, Y., Chen*, L., Schneider, J., Gao, Q., Kannala, J., Schölkopf, B., Pajarinen, J., Büchler, D. Proceedings of the 8th Annual Conference on Robot Learning (CoRL), 270:5184-5203, Proceedings of Machine Learning Research, (Editors: Agrawal, Pulkit and Kroemer, Oliver and Burgard, Wolfram), PMLR, Conference on Robot Learning, November 2024, *equal contribution (Published) URL BibTeX
Empirical Inference Autonomous Learning Conference Paper Learning to Control Emulated Muscles in Real Robots: A Software Test Bed for Bio-Inspired Actuators in Hardware Schumacher, P., Krause, L., Schneider, J., Büchler, D., Martius, G., Haeufle, D. In Proceedings 10th International Conference on Biomedical Robotics and Biomechatronics (BioRob), 806-813, IEEE, 10th International Conference on Biomedical Robotics and Biomechatronics (BioRob), September 2024 (Published) arXiv DOI URL BibTeX
Empirical Inference Learning and Dynamical Systems Robotics Conference Paper Safe & Accurate at Speed with Tendons: A Robot Arm for Exploring Dynamic Motion Guist, S., Schneider, J., Ma, H., Chen, L., Berenz, V., Martus, J., Ott, H., Grüninger, F., Muehlebach, M., Fiene, J., et al. Proceedings of Robotics: Science and Systems, July 2024 (Published) arXiv Project Page DOI URL BibTeX
Empirical Inference Conference Paper Identifying Policy Gradient Subspaces Schneider, J., Schumacher, P., Guist, S., Chen, L., Häufle, D., Schölkopf, B., Büchler, D. The Twelfth International Conference on Learning Representations (ICLR), May 2024 (Published) arXiv BibTeX
Empirical Inference Conference Paper Open X-Embodiment: Robotic Learning Datasets and RT-X Models Open X-Embodiment Collaboration ( incl. Guist, S., Schneider, J., Schölkopf, B., Büchler, D. ). IEEE International Conference on Robotics and Automation (ICRA), 6892-6903, May 2024 (Published) arXiv DOI URL BibTeX
Empirical Inference Learning and Dynamical Systems IT Services Robotics Conference Paper A Robust Open-source Tendon-driven Robot Arm for Learning Control of Dynamic Motions Guist, S., Schneider, J., Ma, H., Berenz, V., Martus, J., Grüninger, F., Muehlebach, M., Fiene, J., Schölkopf, B., Büchler, B. RoboLetics: Workshop on Robot Learning in Athletics @CoRL 2023, November 2023 (Published) URL BibTeX
Empirical Inference Conference Paper Hindsight States: Blending Sim and Real Task Elements for Efficient Reinforcement Learning Guist, S., Schneider, J., Dittrich, A., Berenz, V., Schölkopf, B., Büchler, D. Robotics: Science and Systems XIX, July 2023 (Published) Project Page DOI URL BibTeX
Empirical Inference Conference Paper AIMY: An Open-source Table Tennis Ball Launcher for Versatile and High-fidelity Trajectory Generation Dittrich, A., Schneider, J., Guist, S., Gürtler, N., Ott, H., Steinbrenner, T., Schölkopf, B., Büchler, D. IEEE International Conference on Robotics and Automation (ICRA), 3058-3064, IEEE, June 2023 (Published) arXiv DOI BibTeX
Empirical Inference Conference Paper Investigating the Impact of Action Representations in Policy Gradient Algorithms Schneider, J., Schumacher, P., Häufle, D., Schölkopf, B., Büchler, D. Workshop on effective Representations, Abstractions, and Priors for Robot Learning (RAP4Robots) @ ICRA 2023, May 2023 (Published) arXiv Poster BibTeX
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