每天一篇論文 289/365Deep Reinforcement Learning for Robotic Pushing and Picking in Cluttered Environment
Deep Reinforcement Learning for Robotic Pushing and Picking in Cluttered Environment 摘要 本文提出了一種新的機器人抓取系統，用於在雜亂的場景中自動抓取物體。設計了一種由吸盤和夾持器組成的複合機械手，用於穩定地抓取物體。吸盤首先用於將物體從雜波中提起，並相應地用於抓取物體的夾持器。我們利用提供圖爲吸盤提供像素級提升
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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 295/365Residual Reinforcement Learning for Robot Control
- 3. 論文：Robotic Arm Representation Using Image-Based Feedback for Deep Reinforcement Learning
- 4. 每天一篇論文 350/365 MVP: Unified Motion and Visual Self-Supervised Learning for Large-Scale Robotic Navig
- 5. Policy in Reinforcement Learning
- 6. QUANT[22]論文2:Deep Direct Reinforcement Learning for Financial Signal Representation and Trading
- 7. 每天一篇論文 306/365Self-supervised Learning for Single View Depth and Surface Normal Estimation
- 8. 每天一篇論文 291/365 DSNet: Joint Learning for Scene Segmentation and Disparity Estimation
- 9. 每天一篇論文 310/365 Survey of Model-Based Reinforcement Learning Applications on Robotics
- 10. 讀論文-DARLA:Improving Zero-Shot Transfer in Reinforcement Learning