Pierre Schumacher
Pierre Schumacher
PhD Student @ Max Planck Institute for Intelligent Systems
Verified email at - Homepage
Cited by
Cited by
DEP-RL: Embodied Exploration for Reinforcement Learning in Overactuated and Musculoskeletal Systems
P Schumacher, D F. B. Haeufle, D Büchler, S Schmitt, G Martius
International Conference on Learning Representations (ICLR 2023), 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, 1178-1188, 2023
Natural and Robust Walking using Reinforcement Learning without Demonstrations in High-Dimensional Musculoskeletal Models
P Schumacher, T Geijtenbeek, V Caggiano, V Kumar, S Schmitt, ...
MyoChallenge 2022: Learning contact-rich manipulation using a musculoskeletal hand
V Caggiano, G Durandau, H Wang, A Chiappa, A Mathis, P Tano, N Patel, ...
Proceedings of the NeurIPS 2022 Competitions Track 220, 233-250, 2023
MIMo: A Multi-Modal Infant Model for Studying Cognitive Development
D Mattern, P Schumacher, FM López, MC Raabe, MR Ernst, A Aubret, ...
IEEE Transactions on Cognitive and Developmental Systems, 2024
Investigating the Impact of Action Representations in Policy Gradient Algorithms
J Schneider, P Schumacher, DFB Haeufle, B Schölkopf, D Büchler
Workshop on effective Representations, Abstractions, and Priors for Robot …, 2023
Generating Realistic Arm Movements in Reinforcement Learning: A Quantitative Comparison of Reward Terms and Task Requirements
J Charaja, I Wochner, P Schumacher, W Ilg, M Giese, C Maufroy, ...
arXiv preprint arXiv:2402.13949, 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
arXiv preprint arXiv:2402.05371, 2024
Identifying Policy Gradient Subspaces
J Schneider, P Schumacher, S Guist, L Chen, D Häufle, B Schölkopf, ...
arXiv preprint arXiv:2401.06604, 2024
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