每天一篇论文 289/365Deep Reinforcement Learning for Robotic Pushing and Picking in Cluttered Environment

时间 2021-01-12

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Deep Reinforcement Learning for Robotic Pushing and Picking in Cluttered Environment 摘要本文提出了一种新的机器人抓取系统，用于在杂乱的场景中自动抓取物体。设计了一种由吸盘和夹持器组成的复合机械手，用于稳定地抓取物体。吸盘首先用于将物体从杂波中提起，并相应地用于抓取物体的夹持器。我们利用提供图为吸盘提供像素级提升

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1. 每天一篇论文 296/365 Reinforcement Learning Tracking Control for Robotic Manipulator
2. 每天一篇论文 295/365Residual Reinforcement Learning for Robot Control
3. 论文：Robotic Arm Representation Using Image-Based Feedback for Deep Reinforcement Learning
4. 每天一篇论文 350/365 MVP: Unified Motion and Visual Self-Supervised Learning for Large-Scale Robotic Navig
5. Policy in Reinforcement Learning
6. QUANT[22]论文2:Deep Direct Reinforcement Learning for Financial Signal Representation and Trading
7. 每天一篇论文 306/365Self-supervised Learning for Single View Depth and Surface Normal Estimation
8. 每天一篇论文 291/365 DSNet: Joint Learning for Scene Segmentation and Disparity Estimation
9. 每天一篇论文 310/365 Survey of Model-Based Reinforcement Learning Applications on Robotics
10. 读论文-DARLA:Improving Zero-Shot Transfer in Reinforcement Learning

>>更多相关文章<<