机器人与深度学习

Paper

2017

1. Tateno, K., Tombari, F., Laina, I., & Navab, N. (2017). CNN-SLAM: Real-time dense monocular SLAM with learned depth prediction. arXiv preprint arXiv:1704.03489.
   利用CNN从单目相机中预测深度信息，进行SLAM构建，获得RGBD的三维语义构建.
   http://campar.in.tum.de/Chair/ProjectCNNSLAM

2016

1. From Perception to Decision: A Data-driven Approach to End-to-end Motion Planning for Autonomous Ground Robots - Mark Pfeiffer etc. 2016
   ETH 室内导航, 从感知到决策端到端的自主机器人运动规划

2. Deep Learning for Laser based Odometry Estimation - Nicolai 2016
   激光里程计

3. Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection - Levine , ISER, 2016.
   DeepMind 物体抓取

4. Unsupervised Learning for Physical Interaction through Video Prediction, Chelsea Finn, Ian Goodfellow, Sergey Levine, NIPS, 2016.
   非监督学习物理交互

5. End-to-end training of deep visuomotor policies 2016
   Berkeley 拧瓶盖

6. Sim-to-Real Robot Learning from Pixels with Progressive Nets
   提出了一种叫progressive networks来桥接模拟和现实世界，把模拟环境中学习到的策略转移到现实世界中。Progressive network是一个可以重用把从low-level的视觉特征到high-level的策略转移到新任务上，而且能简单组合实现复杂的技巧的通用框架。

2015

1. Deep Neural Network for Real-Time Autonomous Indoor Navigation - Dong Ki Kim, Tsuhan Chen 2015
   NYTU 无人机室内导航

2. A Machine Learning Approach to the Visual Perception of Forest Trails for Mobile Robots - Alessandro Giusti, Luca M.Gambardella 2015
   无人机导航 深度学习

3. Learning visual odometry with a convolutional network - Konda 2015
   视觉里程计

more

1. Learning monocular reactive uav control in cluttered natural environments 2013
   CMU 无人机穿越森林