An Integrated Simulator and Dataset that Combines Grasping and Vision for Deep Learning

Deep learning is an established framework for learning hierarchical data representations. While compute power is in abundance, one of the main challenges in applying this framework to robotic grasping has been obtaining the amount of data needed to learn these representations, and structuring the data to the task at hand. Among contemporary approaches in the literature, we highlight key properties that have encouraged the use of deep learning techniques, and in this paper, detail our experience in developing a simulator for collecting cylindrical precision grasps of a multi-fingered dexterous robotic hand.

[1]  Vincent Padois,et al.  Tools for dynamics simulation of robots: a survey based on user feedback , 2014, ArXiv.

[2]  Raúl Suárez Feijóo,et al.  Grasp quality measures , 2006 .

[3]  Ole Tange,et al.  GNU Parallel: The Command-Line Power Tool , 2011, login Usenix Mag..

[4]  Danica Kragic,et al.  Data-Driven Grasp Synthesis—A Survey , 2013, IEEE Transactions on Robotics.

[5]  Peter K. Allen,et al.  Graspit! A versatile simulator for robotic grasping , 2004, IEEE Robotics & Automation Magazine.

[6]  Torsten Kröger,et al.  Opening the door to new sensor-based robot applications—The Reflexxes Motion Libraries , 2011, 2011 IEEE International Conference on Robotics and Automation.

[7]  Honglak Lee,et al.  Deep learning for detecting robotic grasps , 2013, Int. J. Robotics Res..

[8]  Anis Sahbani,et al.  An overview of 3D object grasp synthesis algorithms , 2012, Robotics Auton. Syst..

[9]  Morgan Quigley,et al.  ROS: an open-source Robot Operating System , 2009, ICRA 2009.

[10]  Surya P. N. Singh,et al.  V-REP: A versatile and scalable robot simulation framework , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[11]  Sergey Levine,et al.  Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection , 2016, Int. J. Robotics Res..

[12]  Jeannette Bohg,et al.  Leveraging big data for grasp planning , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[13]  Yuval Tassa,et al.  Simulation tools for model-based robotics: Comparison of Bullet, Havok, MuJoCo, ODE and PhysX , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[14]  Takeo Kanade,et al.  Automated Construction of Robotic Manipulation Programs , 2010 .

[15]  Medhat A. Moussa,et al.  Modeling Grasp Motor Imagery Through Deep Conditional Generative Models , 2017, IEEE Robotics and Automation Letters.

[16]  Mohamed S. Kamel,et al.  An experimental approach to robotic grasping using a connectionist architecture and generic grasping functions , 1998, IEEE Trans. Syst. Man Cybern. Part C.

[17]  Benjamin Rosman,et al.  G3DB: A database of successful and failed grasps with RGB-D images, point clouds, mesh models and gripper parameters , 2015 .

[18]  Mathieu Aubry,et al.  Dex-Net 1.0: A cloud-based network of 3D objects for robust grasp planning using a Multi-Armed Bandit model with correlated rewards , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[19]  Abhinav Gupta,et al.  Supersizing self-supervision: Learning to grasp from 50K tries and 700 robot hours , 2015, 2016 IEEE International Conference on Robotics and Automation (ICRA).