Bio
Dieter Büchler is full professor at JKU Linz where he leads the Institute of Machine Intelligence. Before, Dieter was an assistant professor in the Computing Science department at the University of Alberta and a research group leader in the Empirical Inference department at the MPI for Intelligent Systems in Tübingen, Germany. Dieter holds a Canada CIFAR AI chair and is an Alberta Machine Intelligence (Amii) fellow. He earned a Ph.D. in computer science from the TU Darmstadt under Jan Peters and Bernhard Schölkopf and performed the research at the MPI for Intelligent Systems. While pursuing his PhD, Dieter interned at X, the Moonshot Factory (formerly Google X). He received his M.Sc. in Biomedical Engineering from Imperial College London and a B.E. in Information and Electrical Engineering from HAW Hamburg with generous support from Siemens.
His mission is to achieve human performance in athletic, rapidly changing, uncertain, and high-dimensional tasks with physical robots. His research group develops learning approaches for complex systems, like soft and muscular robots, which can excel in these demanding domains. The group also studies how the robotic body influences the acquisition of robotic skills.
News
Mar 2026
Joined Johannes Kepler University Linz as a Full Professor and founded the Institute of Machine Intelligence. We are hiring PhDs and a postdoc (email Dieter directly with a CV, transcripts and a short abstract what research you are interested in)!
Jan 2026
Two papers accepted at ICLR 2026 — on causal reductions for explaining RL policies, and on efficient RL by guiding world models with non-curated data.
Publications Google Scholar ↗
Selected
Learning to Play Table Tennis From Scratch using Muscular Robots
Dieter Büchler, Simon Guist, Roberto Calandra, Vincent Berenz, Bernhard Schölkopf, Jan Peters
IEEE Transactions on Robotics (T-RO), 2022
Safe & Accurate at Speed with Tendons: A Robot Arm for Exploring Dynamic Motion
Simon Guist, Jan Schneider, Hao Ma, Vincent Berenz, Julian Martus, Felix Grüninger, Michael Mühlebach, Jonathan Fiene, Bernhard Schölkopf, Dieter Büchler
Robotics: Science and Systems (R:SS), 2024
Identifying Policy Gradient Subspaces
Jan Schneider, Pierre Schumacher, Simon Guist, Le Chen, Daniel Häufle, Bernhard Schölkopf, Dieter Büchler
International Conference on Learning Representations (ICLR), 2024
RP1M: A Large-Scale Motion Dataset for Piano Playing with Bi-Manual Dexterous Robot Hands
Yi Zhao, Le Chen, Jan Schneider, Quankai Gao, Juho Kannala, Bernhard Schölkopf, Joni Pajarinen, Dieter Büchler
Conference on Robot Learning (CoRL), 2024
Open X-Embodiment: Robotic Learning Datasets and RT-X ModelsBest Paper
Open X-Embodiment Collaboration (incl. Dieter Büchler)
IEEE International Conference on Robotics and Automation (ICRA), 2024
Journal Articles
- Dexterous Robotic Piano Playing at Scale
L. Chen, Y. Zhao, J. Schneider, Q. Gao, S. Guist, C. Qian, J. Kannala, B. Schölkopf, J. Pajarinen, D. Büchler
arXiv · 2025 - Reinforcement Learning with Model-based Feedforward Inputs for Robotic Table Tennis
H. Ma, D. Büchler, B. Schölkopf, M. Mühlebach
Autonomous Robots, 2023 - Learning to Play Table Tennis From Scratch using Muscular Robots
D. Büchler, S. Guist, R. Calandra, V. Berenz, B. Schölkopf, J. Peters
IEEE Transactions on Robotics (T-RO), 2022 - Learning to Control Highly Accelerated Ballistic Movements on Muscular Robots
D. Büchler, R. Calandra, J. Peters
Robotics and Autonomous Systems (RAS), 2022 - The o80 C++ Templated Toolbox: Designing Customized Python APIs for Synchronizing Realtime Processes
V. Berenz, M. Naveau, F. Widmaier, M. Wüthrich, J.C. Passy, S. Guist, D. Büchler
Journal of Open Source Software (JOSS), 2020 - Control of Musculoskeletal Systems using Learned Dynamics Models
D. Büchler, R. Calandra, B. Schölkopf, J. Peters
IEEE Robotics and Automation Letters (RA-L), 2018
Conference Papers
- Sim-to-Real Transfer for Muscle-Actuated Robots via Generalized Actuator Networks
J. Schneider, M. Mahajan, L. Chen, S. Guist, B. Schölkopf, I. Posner, D. Büchler
arXiv · 2026 - Learning Nonlinear Causal Reductions to Explain Reinforcement Learning Policies
A. Kekić, J. Schneider, D. Büchler, B. Schölkopf, M. Besserve
International Conference on Learning Representations (ICLR), 2026 - Efficient Reinforcement Learning by Guiding World Models with Non-Curated Data
Y. Zhao, A. Scannell, T. Cui, L. Chen, D. Büchler, A. Solin, J. Kannala, J. Pajarinen
International Conference on Learning Representations (ICLR), 2026 - RP1M: A Large-Scale Motion Dataset for Piano Playing with Bi-Manual Dexterous Robot Hands
L. Chen, Y. Zhao, J. Schneider, Q. Gao, J. Kannala, B. Schölkopf, J. Pajarinen, D. Büchler
Conference on Robot Learning (CoRL), 2024 - Safe & Accurate at Speed with Tendons: A Robot Arm for Exploring Dynamic Motion
S. Guist, J. Schneider, H. Ma, V. Berenz, J. Martus, F. Grüninger, M. Mühlebach, J. Fiene, B. Schölkopf, D. Büchler
Robotics: Science and Systems (R:SS), 2024 - Identifying Policy Gradient Subspaces
J. Schneider, P. Schumacher, S. Guist, L. Chen, D. Häufle, B. Schölkopf, D. Büchler
International Conference on Learning Representations (ICLR), 2024 - Open X-Embodiment: Robotic Learning Datasets and RT-X ModelsBest Paper
Open X-Embodiment Collaboration (incl. D. Büchler)
IEEE International Conference on Robotics and Automation (ICRA), 2024 - Learning to Control Emulated Muscles in Real Robots: Towards Exploiting Bio-Inspired Actuator Morphology
P. Schumacher, L. Krause, J. Schneider, D. Büchler, G. Martius, D. Haeufle
IEEE Int. Conf. on Biomedical Robotics and Biomechatronics (BioRob), 2024 - Hindsight States: Blending Sim & Real Task Elements for Efficient Reinforcement Learning
S. Guist, J. Schneider, V. Berenz, B. Schölkopf, D. Büchler
Robotics: Science and Systems (R:SS), 2023 - Black-Box vs. Grey-Box: A Case Study on Learning Ping Pong Ball Trajectory Prediction with Spin and Impacts
J. Achterhold, P. Tobuschat, H. Ma, D. Büchler, B. Schölkopf, M. Mühlebach, J. Stückler
Learning for Dynamics and Control (L4DC), 2023 - AIMY: An Open-source Table Tennis Ball Launcher for Versatile and High-fidelity Trajectory Generation
A. Dittrich, J. Schneider, S. Guist, B. Schölkopf, D. Büchler
IEEE International Conference on Robotics and Automation (ICRA), 2023 - Data-Efficient Online Learning of Ball Placement in Robot Table Tennis
P. Tobuschat, H. Ma, D. Büchler, B. Schölkopf, M. Mühlebach
IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2023 - DEP-RL: Embodied Exploration for Reinforcement Learning in Overactuated and Musculoskeletal Systems
P. Schumacher, D. Häufle, D. Büchler, S. Schmitt, G. Martius
International Conference on Learning Representations (ICLR), 2023 - Learning with Muscles: Benefits for Data-Efficiency and Robustness in Anthropomorphic Tasks
I. Wochner, P. Schumacher, G. Martius, D. Büchler, S. Schmitt, D. Haeufle
Conference on Robot Learning (CoRL), 2022 - A Learning-based Iterative Control Framework for Controlling a Robot Arm with Pneumatic Artificial Muscles
H. Ma, D. Büchler, B. Schölkopf, M. Mühlebach
Robotics: Science and Systems (R:SS), 2022 - Hierarchical Reinforcement Learning with Timed Subgoals
N. Gürtler, D. Büchler, G. Martius
Advances in Neural Information Processing Systems (NeurIPS), 2021 - Hidden Parameter Recurrent State Space Models For Changing Dynamics Scenarios
V. Shaj, D. Büchler, R. Sonker, P. Becker, G. Neumann
International Conference on Learning Representations (ICLR), 2021 - Action-Conditional Recurrent Kalman Networks For Forward and Inverse Dynamics Learning
V. Shaj, P. Becker, D. Büchler, H. Pandya, N. van Duijkeren, C.J. Taylor, M. Hanheide, G. Neumann
Conference on Robot Learning (CoRL), 2020 - Jointly Learning Trajectory Generation and Hitting Point Prediction in Robot Table Tennis
Y. Huang, D. Büchler, O. Koç, B. Schölkopf, J. Peters
IEEE-RAS International Conference on Humanoid Robots (Humanoids), 2016 - A Lightweight Robotic Arm with Pneumatic Muscles for Robot Learning
D. Büchler, H. Ott, J. Peters
IEEE International Conference on Robotics and Automation (ICRA), 2016