Jointly learning trajectory generation and hitting point prediction in robot table tennis. - Yanlong Huang; Dieter Buchler; Okan Koc; Bernhard Schölkopf; Jan Peters 001 (Humanoids, 2016)

A lightweight robotic arm with pneumatic muscles for robot learning. - Dieter Buchler; Heiko Ott; Jan Peters 001 (ICRA, 2016)

Control of Musculoskeletal Systems Using Learned Dynamics Models. - Dieter Buchler; Roberto Calandra; Bernhard Schölkopf; Jan Peters 001 (IEEE Robotics Autom. Lett., 2018)

Action-Conditional Recurrent Kalman Networks For Forward and Inverse Dynamics Learning. - Vaisakh Shaj; Philipp Becker; Dieter Büchler; Harit Pandya; Niels van Duijkeren; C. James Taylor; Marc Hanheide; Gerhard Neumann (CoRL, 2020)

Hierarchical Reinforcement Learning with Timed Subgoals. - Nico Gürtler; Dieter Büchler; Georg Martius (NeurIPS, 2021)

The o80 C++ templated toolbox: Designing customized Python APIs for synchronizing realtime processes. - Vincent Berenz; Maximilien Naveau; Felix Widmaier; Manuel Wüthrich; Jean-Claude Passy; Simon Guist; Dieter Büchler (J. Open Source Softw., 2021)

Learning with Muscles: Benefits for Data-Efficiency and Robustness in Anthropomorphic Tasks. - Isabell Wochner; Pierre Schumacher; Georg Martius; Dieter Büchler; Syn Schmitt; Daniel F. B. Haeufle (CoRL, 2022)

Hidden Parameter Recurrent State Space Models For Changing Dynamics Scenarios. - Vaisakh Shaj; Dieter Büchler; Rohit Sonker; Philipp Becker; Gerhard Neumann (ICLR, 2022)

A Learning-based Iterative Control Framework for Controlling a Robot Arm with Pneumatic Artificial Muscles. - Hao Ma; Dieter Büchler; Bernhard Schölkopf; Michael Muehlebach (Robotics: Science and Systems, 2022)

AIMY: An Open-source Table Tennis Ball Launcher for Versatile and High-fidelity Trajectory Generation. - Alexander Dittrich; Jan Schneider 007; Simon Guist; Bernhard Schölkopf; Dieter Büchler (CoRR, 2022)

Learning to Play Table Tennis From Scratch Using Muscular Robots. - Dieter Büchler; Simon Guist; Roberto Calandra; Vincent Berenz; Bernhard Schölkopf; Jan Peters 001 (IEEE Trans. Robotics, 2022)

DEP-RL: Embodied Exploration for Reinforcement Learning in Overactuated and Musculoskeletal Systems. - Pierre Schumacher; Daniel F. B. Haeufle; Dieter Büchler; Syn Schmitt; Georg Martius (ICLR, 2023)

AIMY: An Open-source Table Tennis Ball Launcher for Versatile and High-fidelity Trajectory Generation. - Alexander Dittrich; Jan Schneider 007; Simon Guist; Nico Gürtler; Heiko Ott; Thomas Steinbrenner; Bernhard Schölkopf; Dieter Büchler (ICRA, 2023)

Data-Efficient Online Learning of Ball Placement in Robot Table Tennis. - Philip Tobuschat; Hao Ma; Dieter Büchler; Bernhard Schölkopf; Michael Muehlebach (IROS, 2023)

Black-Box vs. Gray-Box: A Case Study on Learning Table Tennis Ball Trajectory Prediction with Spin and Impacts. - Jan Achterhold; Philip Tobuschat; Hao Ma; Dieter Büchler; Michael Muehlebach; Joerg Stueckler (L4DC, 2023)

Hindsight States: Blending Sim & Real Task Elements for Efficient Reinforcement Learning. - Simon Guist; Jan Schneider 007; Vincent Berenz; Alexander Dittrich; Bernhard Schölkopf; Dieter Büchler (Robotics: Science and Systems, 2023)

Hindsight States: Blending Sim and Real Task Elements for Efficient Reinforcement Learning. - Simon Guist; Jan Schneider 007; Alexander Dittrich; Vincent Berenz; Bernhard Schölkopf; Dieter Büchler (CoRR, 2023)

Synchronizing Machine Learning Algorithms, Realtime Robotic Control and Simulated Environment with o80. - Vincent Berenz; Felix Widmaier; Simon Guist; Bernhard Schölkopf; Dieter Büchler (CoRR, 2023)

A Robust Open-source Tendon-driven Robot Arm for Learning Control of Dynamic Motions. - Simon Guist; Jan Schneider 007; Hao Ma; Vincent Berenz; Julian Martus; Felix Grüninger; Michael Mühlebach; Jonathan Fiene; Bernhard Schölkopf; Dieter Büchler (CoRR, 2023)

Investigating the Impact of Action Representations in Policy Gradient Algorithms. - Jan Schneider 007; Pierre Schumacher; Daniel F. B. Häufle; Bernhard Schölkopf; Dieter Büchler (CoRR, 2023)

Open X-Embodiment: Robotic Learning Datasets and RT-X Models. - Open X.-Embodiment Collaboration; Abhishek Padalkar; Acorn Pooley; Ajinkya Jain; Alex Bewley; Alexander Herzog; Alex Irpan; Alexander Khazatsky; Anant Raj; Anikait Singh; Anthony Brohan; Antonin Raffin; et al. (CoRR, 2023)

Learning to Control Highly Accelerated Ballistic Movements on Muscular Robots. - Dieter Büchler; Roberto Calandra; Jan Peters 001 (Robotics Auton. Syst., 2023)

Reinforcement learning with model-based feedforward inputs for robotic table tennis. - Hao Ma; Dieter Büchler; Bernhard Schölkopf; Michael Muehlebach (Auton. Robots, 2023)

Learning to Control Emulated Muscles in Real Robots: A Software Test Bed for Bio-Inspired Actuators in Hardware. - Pierre Schumacher; Lorenz Krause; Jan Schneider 007; Dieter Büchler; Georg Martius; Daniel F. B. Haeufle (BioRob, 2024)

RP1M: A Large-Scale Motion Dataset for Piano Playing with Bi-Manual Dexterous Robot Hands. - Yi Zhao 014; Le Chen; Jan Schneider 007; Quankai Gao; Juho Kannala; Bernhard Schölkopf; Joni Pajarinen; Dieter Büchler (CoRL, 2024)

Identifying Policy Gradient Subspaces. - Jan Schneider 007; Pierre Schumacher; Simon Guist; Le Chen; Daniel F. B. Haeufle; Bernhard Schölkopf; Dieter Büchler (ICLR, 2024)

Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration. - Abby O'Neill; Abdul Rehman; Abhiram Maddukuri; Abhishek Gupta 004; Abhishek Padalkar; Abraham Lee; Acorn Pooley; Agrim Gupta; Ajay Mandlekar; Ajinkya Jain; Albert Tung; Alex Bewley; et al. (ICRA, 2024)

Safe & Accurate at Speed with Tendons: A Robot Arm for Exploring Dynamic Motion. - Simon Guist; Jan Schneider 007; Hao Ma; Le Chen; Vincent Berenz; Julian Martus; Heiko Ott; Felix Grüninger; Michael Muehlebach; Jonathan Fiene; Bernhard Schölkopf; Dieter Büchler (Robotics: Science and Systems, 2024)

Learning to Control Emulated Muscles in Real Robots: Towards Exploiting Bio-Inspired Actuator Morphology. - Pierre Schumacher; Lorenz Krause; Jan Schneider 007; Dieter Büchler; Georg Martius; Daniel F. B. Haeufle (CoRR, 2024)

Generalist World Model Pre-Training for Efficient Reinforcement Learning. - Yi Zhao 014; Aidan Scannell; Yuxin Hou; Tianyu Cui; Le Chen; Dieter Büchler; Arno Solin; Juho Kannala; Joni Pajarinen (CoRR, 2025)

Learning Nonlinear Causal Reductions to Explain Reinforcement Learning Policies. - Armin Kekic; Jan Schneider 007; Dieter Büchler; Bernhard Schölkopf; Michel Besserve (CoRR, 2025)