每天一篇论文 295/365Residual Reinforcement Learning for Robot Control
Residual Reinforcement Learning for Robot Control 摘要 传统的反馈控制方法通过捕捉具有显式模型（如刚体运动方程）的结构，可以非常有效地解决各种类型的机器人控制问题。然而，在现代制造业中，许多控制问题涉及接触和摩擦，这是一阶物理建模难以捕捉的。因此，将控制设计方法应用于此类问题通常会导致控制器脆弱且不准确，必须手动调整以进行部署。强化学习（RL）方法
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相关文章
- 1. 每天一篇论文 296/365 Reinforcement Learning Tracking Control for Robotic Manipulator
- 2. 每天一篇论文 289/365Deep Reinforcement Learning for Robotic Pushing and Picking in Cluttered Environment
- 3. [Reinforcement Learning] Model-Free Control
- 4. 每天一篇论文 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分钟 Paper】Continuous Control With Deep Reinforcement Learning
- 8. 论文：Robotic Arm Representation Using Image-Based Feedback for Deep Reinforcement Learning
- 9. 每天一篇论文 306/365Self-supervised Learning for Single View Depth and Surface Normal Estimation
- 10. 每天一篇论文 291/365 DSNet: Joint Learning for Scene Segmentation and Disparity Estimation