Learning robots to grasp by demonstration

Abstract In recent years, we have witnessed the proliferation of so-called collaborative robots or cobots, that are designed to work safely along with human operators. These cobots typically use the “program from demonstration” paradigm to record and replay trajectories, rather than the traditional source-code based programming approach. While this requires less knowledge from the operator, the basic functionality of a cobot is limited to simply replay the sequence of actions as they were recorded. In this paper, we present a system that mitigates this restriction and learns to grasp an arbitrary object from visual input using demonstrated examples. While other learning-based approaches for robotic grasping require collecting a large amount of examples, either manually or automatically harvested in a real or simulated world, our approach learns to grasp from a single demonstration with the ability to improve on accuracy using additional input samples. We demonstrate grasping of various objects with the Franka Panda collaborative robot. We show that the system is able to grasp various objects from demonstration, regardless their position and rotation in less than 5 min of training time on a NVIDIA Titan X GPU, achieving over 90% average success rate.

[1]  Stefan Schaal,et al.  Learning from Demonstration , 1996, NIPS.

[2]  Alessandro De Luca,et al.  Collision detection and reaction: A contribution to safe physical Human-Robot Interaction , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[3]  Weidong Li,et al.  Cobot programming for collaborative industrial tasks: An overview , 2019, Robotics Auton. Syst..

[4]  Stefan Schaal,et al.  2008 Special Issue: Reinforcement learning of motor skills with policy gradients , 2008 .

[5]  Stefan Schaal,et al.  Learning and generalization of motor skills by learning from demonstration , 2009, 2009 IEEE International Conference on Robotics and Automation.

[6]  Andrew Zisserman,et al.  Return of the Devil in the Details: Delving Deep into Convolutional Nets , 2014, BMVC.

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

[8]  Peter Corke,et al.  Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approach , 2018, Robotics: Science and Systems.

[9]  Richard Bloss,et al.  Collaborative robots are rapidly providing major improvements in productivity, safety, programing ease, portability and cost while addressing many new applications , 2016, Ind. Robot.

[10]  Subhashis Banerjee,et al.  Active recognition through next view planning: a survey , 2004, Pattern Recognit..

[11]  Rouhollah Rahmatizadeh,et al.  Vision-Based Multi-Task Manipulation for Inexpensive Robots Using End-to-End Learning from Demonstration , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[12]  Filip De Turck,et al.  AIOLOS: Middleware for improving mobile application performance through cyber foraging , 2012, J. Syst. Softw..

[13]  Osgi Alliance,et al.  Osgi Service Platform, Release 3 , 2003 .

[14]  Jianbin Tang,et al.  GraspNet: An Efficient Convolutional Neural Network for Real-time Grasp Detection for Low-powered Devices , 2018, IJCAI.

[15]  Sergey Levine,et al.  Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates , 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).

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

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

[18]  Ashutosh Saxena,et al.  Robotic Grasping of Novel Objects using Vision , 2008, Int. J. Robotics Res..

[19]  Sergey Levine,et al.  QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation , 2018, CoRL.

[20]  Boris Otto,et al.  Design Principles for Industrie 4.0 Scenarios: A Literature Review , 2015 .

[21]  Peter K. Allen,et al.  Generating multi-fingered robotic grasps via deep learning , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[22]  Steven Bohez,et al.  Middleware Platform for Distributed Applications Incorporating Robots, Sensors and the Cloud , 2016, 2016 5th IEEE International Conference on Cloud Networking (Cloudnet).

[23]  Hossein Sharifi,et al.  Agile manufacturing in practice ‐ Application of a methodology , 2001 .

[24]  Steven Bohez,et al.  DIANNE: a modular framework for designing, training and deploying deep neural networks on heterogeneous distributed infrastructure , 2018, J. Syst. Softw..

[25]  Joseph Redmon,et al.  Real-time grasp detection using convolutional neural networks , 2014, 2015 IEEE International Conference on Robotics and Automation (ICRA).

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

[27]  Tim Verbelen,et al.  Learning to Grasp from a Single Demonstration , 2018, ArXiv.

[28]  Peter I. Corke,et al.  The ACRV picking benchmark: A robotic shelf picking benchmark to foster reproducible research , 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[29]  Rama Chellappa,et al.  Designing Deep Convolutional Neural Networks for Continuous Object Orientation Estimation , 2017, ArXiv.

[30]  Danica Kragic,et al.  Demonstration-based learning and control for automatic grasping , 2009, Intell. Serv. Robotics.

[31]  Van-Duc Nguyen,et al.  Constructing force-closure grasps , 1986, Proceedings. 1986 IEEE International Conference on Robotics and Automation.

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

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

[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]  Sergey Levine,et al.  Learning Hand-Eye Coordination for Robotic Grasping with Large-Scale Data Collection , 2016, ISER.

[36]  Peter I. Corke,et al.  Multi-View Picking: Next-best-view Reaching for Improved Grasping in Clutter , 2018, 2019 International Conference on Robotics and Automation (ICRA).