Jan Schneider

Affiliations:
  • Max Planck Institute for Intelligent Systems, Tübingen, Germany


According to our database1, Jan Schneider authored at least 14 papers between 2022 and 2025.

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Bibliography

2025
Dexterous Robotic Piano Playing at Scale.
CoRR, November, 2025

Learning Nonlinear Causal Reductions to Explain Reinforcement Learning Policies.
CoRR, July, 2025

2024
Learning to Control Emulated Muscles in Real Robots: Towards Exploiting Bio-Inspired Actuator Morphology.
CoRR, 2024

Safe & Accurate at Speed with Tendons: A Robot Arm for Exploring Dynamic Motion.
Proceedings of the Robotics: Science and Systems XX, 2024

Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration.
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Proceedings of the IEEE International Conference on Robotics and Automation, 2024

Identifying Policy Gradient Subspaces.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

RP1M: A Large-Scale Motion Dataset for Piano Playing with Bi-Manual Dexterous Robot Hands.
Proceedings of the Conference on Robot Learning, 6-9 November 2024, Munich, Germany., 2024

Learning to Control Emulated Muscles in Real Robots: A Software Test Bed for Bio-Inspired Actuators in Hardware.
Proceedings of the 10th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics, 2024

2023
Investigating the Impact of Action Representations in Policy Gradient Algorithms.
CoRR, 2023

A Robust Open-source Tendon-driven Robot Arm for Learning Control of Dynamic Motions.
CoRR, 2023

Hindsight States: Blending Sim and Real Task Elements for Efficient Reinforcement Learning.
CoRR, 2023

Hindsight States: Blending Sim & Real Task Elements for Efficient Reinforcement Learning.
Proceedings of the Robotics: Science and Systems XIX, Daegu, 2023

AIMY: An Open-source Table Tennis Ball Launcher for Versatile and High-fidelity Trajectory Generation.
Proceedings of the IEEE International Conference on Robotics and Automation, 2023

2022
AIMY: An Open-source Table Tennis Ball Launcher for Versatile and High-fidelity Trajectory Generation.
CoRR, 2022


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