每天一篇論文 295/365Residual Reinforcement Learning for Robot Control
Residual Reinforcement Learning for Robot Control 摘要 傳統的反饋控制方法通過捕捉具有顯式模型（如剛體運動方程）的結構，可以非常有效地解決各種類型的機器人控制問題。然而，在現代製造業中，許多控制問題涉及接觸和摩擦，這是一階物理建模難以捕捉的。因此，將控制設計方法應用於此類問題通常會導致控制器脆弱且不準確，必須手動調整以進行部署。強化學習（RL）方法
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相關文章
- 1. 每天一篇論文 296/365 Reinforcement Learning Tracking Control for Robotic Manipulator
- 2. 每天一篇論文 289/365Deep Reinforcement Learning for Robotic Pushing and Picking in Cluttered Environment
- 3. [Reinforcement Learning] Model-Free Control
- 4. 每天一篇論文 310/365 Survey of Model-Based Reinforcement Learning Applications on Robotics
- 5. Reinforcement Learning: Model-free control
- 6. Continuous control with Deep Reinforcement Learning
- 7. 【5分鐘 Paper】Continuous Control With Deep Reinforcement Learning
- 8. 論文：Robotic Arm Representation Using Image-Based Feedback for Deep Reinforcement Learning
- 9. 每天一篇論文 306/365Self-supervised Learning for Single View Depth and Surface Normal Estimation
- 10. 每天一篇論文 291/365 DSNet: Joint Learning for Scene Segmentation and Disparity Estimation