A cloud robot system using the dexterity network and berkeley robotics and automation as a service (Brass)

In support of Cloud Robotics, Robotics and Automation as a Service (RAaaS) frameworks have the potential to reduce the complexity of software development, simplify software installation and maintenance, and facilitate data sharing for machine learning. In this proof-of-concept paper, we describe Berkeley Robotics and Automation as a Service (Brass), a RAaaS prototype that allows robots to access a remote server that hosts a robust grasp-planning system (Dex-Net 1.0) that maintains data on hundreds of candidate grasps on thousands of 3D object meshes and uses perturbation sampling to estimate and update a stochastic robustness metric for each grasp. Results suggest that such a system can increase grasp reliability over naive locally-computed grasping strategies with network latencies of 30 and 200 msec for servers 500 and 6000 miles away, respectively. We also study how the system can use execution reports from robots in the field to update grasp recommendations over time.

[1]  A. S. Rao,et al.  Computing a Statistical Distribution of Stable Poses for a Polyhedron , 1992 .

[2]  John F. Canny,et al.  Planning optimal grasps , 1992, Proceedings 1992 IEEE International Conference on Robotics and Automation.

[3]  Richard M. Murray,et al.  A Mathematical Introduction to Robotic Manipulation , 1994 .

[4]  Masayuki Inaba,et al.  Remote-Brained Robots , 1997, IJCAI.

[5]  Vijay Kumar,et al.  Robotic grasping and contact: a review , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[6]  Roland Siegwart,et al.  Beyond Webcams: An Introduction to Online Robots , 2001 .

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

[8]  Xiaojun Wu,et al.  DAvinCi: A cloud computing framework for service robots , 2010, 2010 IEEE International Conference on Robotics and Automation.

[9]  Peter K. Allen,et al.  Data-driven grasping , 2011, Auton. Robots.

[10]  R. D’Andrea,et al.  A World Wide Web for Robots • , 2011 .

[11]  James J. Kuffner,et al.  Physically-based grasp quality evaluation under uncertainty , 2012, 2012 IEEE International Conference on Robotics and Automation.

[12]  Peter K. Allen,et al.  Pose error robust grasping from contact wrench space metrics , 2012, 2012 IEEE International Conference on Robotics and Automation.

[13]  Moritz Tenorth,et al.  The RoboEarth language: Representing and exchanging knowledge about actions, objects, and environments , 2012, 2012 IEEE International Conference on Robotics and Automation.

[14]  Peter K. Allen,et al.  Learning grasp stability , 2012, 2012 IEEE International Conference on Robotics and Automation.

[15]  Kenneth Y. Goldberg,et al.  Cloud-based robot grasping with the google object recognition engine , 2013, 2013 IEEE International Conference on Robotics and Automation.

[16]  James J. Kuffner,et al.  Physically Based Grasp Quality Evaluation Under Pose Uncertainty , 2013, IEEE Transactions on Robotics.

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

[18]  Benjamin Robert Kehoe,et al.  Cloud-based Methods and Architectures for Robot Grasping , 2014 .

[19]  Lino Marques,et al.  Computation Sharing in Distributed Robotic Systems: A Case Study on SLAM , 2015, IEEE Transactions on Automation Science and Engineering.

[20]  Jörg Krüger,et al.  Robot control as a service — Towards cloud-based motion planning and control for industrial robots , 2015, 2015 10th International Workshop on Robot Motion and Control (RoMoCo).

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

[22]  Danica Kragic,et al.  Multi-armed bandit models for 2D grasp planning with uncertainty , 2015, 2015 IEEE International Conference on Automation Science and Engineering (CASE).

[23]  Pieter Abbeel,et al.  Image Object Label 3 D CAD Model Candidate Grasps Google Object Recognition Engine Google Cloud Storage Select Feasible Grasp with Highest Success Probability Pose EstimationCamera Robots Cloud 3 D Sensor , 2014 .

[24]  Cezary Zielinski,et al.  Reconfigurable control architecture for exploratory robots , 2015, 2015 10th International Workshop on Robot Motion and Control (RoMoCo).

[25]  Raffaello D'Andrea,et al.  Rapyuta: A Cloud Robotics Platform , 2015, IEEE Transactions on Automation Science and Engineering.

[26]  Kostas E. Bekris,et al.  Cloud Automation: Precomputing Roadmaps for Flexible Manipulation , 2015, IEEE Robotics & Automation Magazine.

[27]  Ron Alterovitz,et al.  Cloud-based Motion Plan Computation for Power-Constrained Robots , 2016, WAFR.

[28]  Basilio Bona,et al.  A Novel Cloud-Based Service Robotics Application to Data Center Environmental Monitoring , 2016, Sensors.

[29]  Stefan Leutenegger,et al.  Deep learning a grasp function for grasping under gripper pose uncertainty , 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[30]  Stefano Rosa,et al.  Fly4SmartCity: A cloud robotics service for smart city applications , 2016, J. Ambient Intell. Smart Environ..

[31]  Tim Kraska,et al.  PrivateClean: Data Cleaning and Differential Privacy , 2016, SIGMOD Conference.

[32]  Fulong Chen,et al.  Cloud Robotics: Insight and Outlook , 2016 .

[33]  Arjun Kumar Singh,et al.  Benchmarks for Cloud Robotics , 2016 .

[34]  Sanjay Krishnan,et al.  ActiveClean: Interactive Data Cleaning For Statistical Modeling , 2016, Proc. VLDB Endow..

[35]  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).

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