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Differentiable Physics Models for Real-world Offline Model-based Reinforcement Learning.

, , , and . ICRA, page 4163-4170. IEEE, (2021)

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Differentiable Physics Models for Real-world Offline Model-based Reinforcement Learning., , , and . ICRA, page 4163-4170. IEEE, (2021)Online Learning of an Open-Ended Skill Library for Collaborative Tasks., , , , and . Humanoids, page 1-9. IEEE, (2018)Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning., , and . ICLR (Poster), OpenReview.net, (2019)Inductive Biases in Machine Learning for Robotics and Control. TU Darmstadt, Germany, (2021)base-search.net (ftulbdarmstadt:oai:tuprints.ulb.tu-darmstadt.de:20048).Deep Lagrangian Networks for end-to-end learning of energy-based control for under-actuated systems., , and . IROS, page 7718-7725. IEEE, (2019)Robust Value Iteration for Continuous Control Tasks., , , , and . Robotics: Science and Systems, (2021)Diminishing Return of Value Expansion Methods in Model-Based Reinforcement Learning., , , and . ICLR, OpenReview.net, (2023)Differentiable Physics Models for Real-world Offline Model-based Reinforcement Learning., , , and . CoRR, (2020)HJB Optimal Feedback Control with Deep Differential Value Functions and Action Constraints., , , , and . CoRL, volume 100 of Proceedings of Machine Learning Research, page 640-650. PMLR, (2019)Trajectory Optimization of Energy Consumption and Expected Service Life of a Robotic System., , , , and . AIM, page 842-847. IEEE, (2021)