It is my reading with RL, AI papers. There have 7 levels to explain the myself learning on reachers.
There are few class :
Grasping
Computer vision
Transfering and Muilt-task learning
Manipulation
Exploration
Sim2real
Others
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Deep Reinforcement Learning for Vision-Based Robotic Grasping: A Simulated Comparative Evaluation of Off-Policy Methods
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Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection
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A Framework for Efficient Robotic Manipulation
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Grasp Proposal Networks- An End-to-End Solution for Visual Learning of Robotic Grasps
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QT-Opt Scalable Deep_Reinforcement Learning for Vision-Based Robotic Manipulation
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Dex-Net 2.0: Deep Learning to Plan Robust Grasps withSynthetic Point Clouds and Analytic Grasp Metrics
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Dex-Net 2.1: Learning Deep Policies for Robot Bin Picking by Simulating Robust Grasping Sequences
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Dex-Net 3.0: Computing Robust Robot Suction Grasp Targets using a New Analytic Model and Deep Learning
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Combining Deep Deterministic Policy Gradient with Cross-Entropy Method
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Learning_Hand-Eye_Coordination_for_Robotic_Grasping_with_Deep_Learning_and_Large-Scale_Data_Collection
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You Only Look Once: Unified, Real-Time Object Detection
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YOLO9000: Better, Faster, Stronger
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YOLOv3: An Incremental Improvement
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Mask R-CNN
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Transfering and Muilt-task learning
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EPOpt: Learning robust neural network policies..
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Sim-to-Real Transfer of Robotic Control with Dynamics Randomization
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Adapting Visuomotor Representations with Weak Pairwise Constraints
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Transporter Networks- Rearranging the Visual World for Robotic Manipulation
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Robotic Table Tennis with Model-Free Reinforcement Learning
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Deep Exploration via Bootstrapped DQN
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VIME_Variational Information Maximizing Exploration
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Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping
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3D Simulation for Robot Arm Control with Deep Q-Learning
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RL-CycleGAN Reinforcement Learning_Aware Simulation_To_Real
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RetinaGAN An Object-aware Approach to Sim-to-Real Transfer
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Sim-To-Real Transfer for Miniature AutonomousCar Racing
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Learning to Stop: Dynamic Simulation Monte-Carlo Tree Search
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An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition
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ClearGrasp 3D Shape Estimation of Transparent Objects for Manipulation
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