PyRobot: An Open-source Robotics Framework for Research and Benchmarking

This paper introduces PyRobot, an open-source robotics framework for research and benchmarking. PyRobot is a light-weight, high-level interface on top of ROS that provides a consistent set of hardware independent mid-level APIs to control different robots. PyRobot abstracts away details about low-level controllers and inter-process communication, and allows non-robotics researchers (ML, CV researchers) to focus on building high-level AI applications. PyRobot aims to provide a research ecosystem with convenient access to robotics datasets, algorithm implementations and models that can be used to quickly create a state-of-the-art baseline. We believe PyRobot, when paired up with low-cost robot platforms such as LoCoBot, will reduce the entry barrier into robotics, and democratize robotics. PyRobot is open-source, and can be accessed via this https URL.

[1]  K. Åström.  Introduction to Stochastic Control Theory , 1970 .

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

[3]  Luc Van Gool,et al.  The Pascal Visual Object Classes Challenge: A Retrospective , 2014, International Journal of Computer Vision.

[4]  Pietro Perona,et al.  Microsoft COCO: Common Objects in Context , 2014, ECCV.

[5]  Abhinav Gupta,et al.  Robot Learning in Homes: Improving Generalization and Reducing Dataset Bias , 2018, NeurIPS.

[6]  Andrew Howard,et al.  Design and use paradigms for Gazebo, an open-source multi-robot simulator , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[7]  Lydia E. Kavraki,et al.  The Open Motion Planning Library , 2012, IEEE Robotics & Automation Magazine.

[8]  Wolfram Burgard,et al.  The dynamic window approach to collision avoidance , 1997, IEEE Robotics Autom. Mag..

[9]  Ashish Sharma,et al.  An Enhanced Density Based Spatial Clustering of Applications with Noise , 2009, 2009 IEEE International Advance Computing Conference.

[10]  Trevor Darrell,et al.  Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.

[11]  Rodney A. Brooks,et al.  A Robust Layered Control Syste For A Mobile Robot , 2022 .

[12]  Luca Antiga,et al.  Automatic differentiation in PyTorch , 2017 .

[13]  Douwe Kiela,et al.  SentEval: An Evaluation Toolkit for Universal Sentence Representations , 2018, LREC.

[14]  Herman Bruyninckx,et al.  Open robot control software: the OROCOS project , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[15]  Jitendra Malik,et al.  Learning to Poke by Poking: Experiential Learning of Intuitive Physics , 2016, NIPS.

[16]  Carl E. Rasmussen,et al.  Learning to Control a Low-Cost Manipulator using Data-Efficient Reinforcement Learning , 2011, Robotics: Science and Systems.

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

[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]  Joonho Lee,et al.  Learning agile and dynamic motor skills for legged robots , 2019, Science Robotics.

[20]  Just L. Herder,et al.  Ability to hold grasped objects by underactuated hands: Performance prediction and experiments , 2009, 2009 IEEE International Conference on Robotics and Automation.

[21]  Jitendra Malik,et al.  Gibson Env: Real-World Perception for Embodied Agents , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[22]  Mihai Surdeanu,et al.  The Stanford CoreNLP Natural Language Processing Toolkit , 2014, ACL.

[23]  Blake Hannaford,et al.  Raven-II: An Open Platform for Surgical Robotics Research , 2013, IEEE Transactions on Biomedical Engineering.

[24]  Henry Zhu,et al.  Dexterous Manipulation with Deep Reinforcement Learning: Efficient, General, and Low-Cost , 2018, 2019 International Conference on Robotics and Automation (ICRA).

[25]  Sergey Levine,et al.  Deep visual foresight for planning robot motion , 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[26]  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).

[27]  James Davidson,et al.  Supervision via competition: Robot adversaries for learning tasks , 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[28]  Abhinav Gupta,et al.  The Curious Robot: Learning Visual Representations via Physical Interactions , 2016, ECCV.

[29]  James J. Kuffner,et al.  OpenRAVE: A Planning Architecture for Autonomous Robotics , 2008 .

[30]  Matei T. Ciocarlie,et al.  The Columbia grasp database , 2009, 2009 IEEE International Conference on Robotics and Automation.

[31]  Sergey Levine,et al.  End-to-End Training of Deep Visuomotor Policies , 2015, J. Mach. Learn. Res..

[32]  Yuval Tassa,et al.  MuJoCo: A physics engine for model-based control , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[33]  Omer Levy,et al.  GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding , 2018, BlackboxNLP@EMNLP.

[34]  Xinyu Liu,et al.  Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics , 2017, Robotics: Science and Systems.

[35]  Pericles A. Mitkas,et al.  Robotic frameworks, architectures and middleware comparison , 2017, ArXiv.

[36]  Sergey Levine,et al.  REPLAB: A Reproducible Low-Cost Arm Benchmark Platform for Robotic Learning , 2019, ArXiv.

[37]  Jitendra Malik,et al.  Habitat: A Platform for Embodied AI Research , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[38]  Siddhartha S. Srinivasa,et al.  Benchmarking in Manipulation Research: Using the Yale-CMU-Berkeley Object and Model Set , 2015, IEEE Robotics & Automation Magazine.

[39]  Rüdiger Dillmann,et al.  The KIT object models database: An object model database for object recognition, localization and manipulation in service robotics , 2012, Int. J. Robotics Res..

[40]  Pieter Abbeel,et al.  Quasi-Direct Drive for Low-Cost Compliant Robotic Manipulation , 2019, 2019 International Conference on Robotics and Automation (ICRA).

[41]  Juan D. Tardós,et al.  ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras , 2016, IEEE Transactions on Robotics.

[42]  Rahul Sukthankar,et al.  Cognitive Mapping and Planning for Visual Navigation , 2017, International Journal of Computer Vision.

[43]  Sachin Chitta,et al.  MoveIt! [ROS Topics] , 2012, IEEE Robotics Autom. Mag..

[44]  Beatrice Santorini,et al.  Building a Large Annotated Corpus of English: The Penn Treebank , 1993, CL.

[45]  OpenAI.  Learning Dexterous In-Hand Manipulation. , 2018 .

[46]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[47]  Oliver Brock,et al.  Guest Editorial Open Discussion of Robot Grasping Benchmarks, Protocols, and Metrics , 2018, IEEE Trans Autom. Sci. Eng..

[48]  Jakub W. Pachocki,et al.  Learning dexterous in-hand manipulation , 2018, Int. J. Robotics Res..

[49]  Jitendra Malik,et al.  A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.