Michael Lutter

収録論文 12本 ・ フィジカルAI/ロボット学習

※arXiv著者名で収集。同姓同名の別人の論文が含まれる場合があります。

論文

• Combining Physics and Deep Learning to learn Continuous-Time Dynamics Models
  2021/10/1
• A Differentiable Newton-Euler Algorithm for Real-World Robotics
  2021/10/1
• Continuous-Time Fitted Value Iteration for Robust Policies
  2021/10/1
• Learning Dynamics Models for Model Predictive Agents
  2021/9/1
• Robust Value Iteration for Continuous Control Tasks
  2021/5/1
• Value Iteration in Continuous Actions, States and Time
  2021/5/1
• Differentiable Physics Models for Real-world Offline Model-based Reinforcement Learning
  2020/11/1
• High Acceleration Reinforcement Learning for Real-World Juggling with Binary Rewards
  2020/10/1
• A Differentiable Newton Euler Algorithm for Multi-body Model Learning
  2020/10/1
• HJB Optimal Feedback Control with Deep Differential Value Functions and Action Constraints
  2019/9/1
• Deep Lagrangian Networks for end-to-end learning of energy-based control for under-actuated systems
  2019/7/1
• Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning
  2019/7/1