Michael Lutter

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研究焦点

主要成就

代表论文

1. 1
   Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning

   171 次引用 · 2023
2. 2
   Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning

   81 次引用 · 2019
3. 3
   Combining physics and deep learning to learn continuous-time dynamics models

   53 次引用 · 2023
4. 4
   Online Learning of an Open-Ended Skill Library for Collaborative Tasks

   16 次引用 · 2018
5. 5
   High Acceleration Reinforcement Learning for Real-World Juggling with\n Binary Rewards

   13 次引用 · 2020
6. 6
   HJB Optimal Feedback Control with Deep Differential Value Functions and Action Constraints.

   6 次引用 · 2019
7. 7
   A Differentiable Newton–Euler Algorithm for Real-World Robotics

   6 次引用 · 2023
8. 8
   Building Skill Learning Systems for Robotics

   5 次引用 · 2021
9. 9
   Deep Lagrangian Networks for end-to-end learning of energy-based control for under-actuated systems

   5 次引用 · 2019
10. 10
    High Acceleration Reinforcement Learning for Real-World Juggling with Binary Rewards

    5 次引用 · 2020

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