Towards a Sample Efficient Reinforcement Learning Pipeline for Vision Based Robotics
arXiv:2105.09719
Abstract
Deep Reinforcement learning holds the guarantee of empowering self-ruling robots to master enormous collections of conduct abilities with negligible human mediation. The improvements brought by this technique enables robots to perform difficult tasks such as grabbing or reaching targets. Nevertheless, the training process is still time consuming and tedious especially when learning policies only with RGB camera information. This way of learning is capital to transfer the task from simulation to the real world since the only external source of information for the robot in real life is video. In this paper, we study how to limit the time taken for training a robotic arm with 6 Degrees Of Freedom (DOF) to reach a ball from scratch by assembling a pipeline as efficient as possible. The pipeline is divided into two parts: the first one is to capture the relevant information from the RGB video with a Computer Vision algorithm. The second one studies how to train faster a Deep Reinforcement Learning algorithm in order to make the robotic arm reach the target in front of him. Follow this link to find videos and plots in higher resolution: \url{https://drive.google.com/drive/folders/1_lRlDSoPzd_GTcVrxNip10o_lm-_DPdn?usp=sharing}
arXiv comment10 Pages, 15 Figures, 1 Table
References in corpus (12)
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
- Rethinking Atrous Convolution for Semantic Image Segmentation
- Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
- Addressing Function Approximation Error in Actor-Critic Methods
- Big Self-Supervised Models are Strong Semi-Supervised Learners
- Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control
- Virtual to Real Reinforcement Learning for Autonomous Driving
- Safe and Nested Subgame Solving for Imperfect-Information Games
- PyRep: Bringing V-REP to Deep Robot Learning
- Learning Visual Robotic Control Efficiently with Contrastive Pre-training and Data Augmentation
- OptLayer - Practical Constrained Optimization for Deep Reinforcement Learning in the Real World