Current Research Trends in Robot Grasping and Bin Picking

We provide a view of current research issues in Robotic Grasping and Bin Picking focused on the perception aspects of the problem, mainly related to computer vision algorithms. After recalling the evolution of the topics in the last decades, we focus on the modern use of Deep Learning Algorithms. Two main trends are followed in the approaches to innovative grasping techniques. First, Convolutional Neural Networks are used for grasping perceptual aspects. We discuss the different degrees of success of several published approaches. Second, Deep Reinforcement Learning is being extensively tested in order to develop integrated eye-hand coordination systems not requiring delicate calibration. We provide also a discussion of possible future lines of research.

[1]  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.

[2]  Kazuhito Yokoi,et al.  Force Strategies for Cooperative Tasks in Multiple Mobile Manipulation Systems , 1996 .

[3]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[4]  Chong Wang,et al.  Deep Speech 2 : End-to-End Speech Recognition in English and Mandarin , 2015, ICML.

[5]  Gérard G. Medioni,et al.  Object modelling by registration of multiple range images , 1992, Image Vis. Comput..

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

[7]  Markus Vincze,et al.  3DNet: Large-scale object class recognition from CAD models , 2012, 2012 IEEE International Conference on Robotics and Automation.

[8]  Berthold K. P. Horn,et al.  The Mechanical Manipulation of Randomly Oriented Parts , 1984 .

[9]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[10]  Ales Leonardis,et al.  Task-relevant grasp selection: A joint solution to planning grasps and manipulative motion trajectories , 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[11]  Hideo Hanafusa,et al.  Prehension and Handling of Objects by Robot Hands with Redundant Fingers , 1979 .

[12]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[13]  Anca D. Dragan,et al.  Robot grasping in clutter: Using a hierarchy of supervisors for learning from demonstrations , 2016, 2016 IEEE International Conference on Automation Science and Engineering (CASE).

[14]  Matthew T. Mason,et al.  Robot Hands and the Mechanics of Manipulation , 1985 .

[15]  Peter K. Allen,et al.  Examples of 3D grasp quality computations , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[16]  Demis Hassabis,et al.  Mastering the game of Go without human knowledge , 2017, Nature.

[17]  M. Hebert,et al.  The Representation, Recognition, and Locating of 3-D Objects , 1986 .

[18]  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).

[19]  浅田 春比古,et al.  Studies on prehension and handling by robot hands with elastic fingers , 1979 .

[20]  Paul F. Whelan,et al.  A Systems Engineering Approach to Robotic Bin Picking , 2008 .

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

[22]  Nassir Navab,et al.  Model globally, match locally: Efficient and robust 3D object recognition , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[23]  Kate Saenko,et al.  High precision grasp pose detection in dense clutter , 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

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

[25]  Lawrence G. Roberts,et al.  Machine Perception of Three-Dimensional Solids , 1963, Outstanding Dissertations in the Computer Sciences.

[26]  Tsuneo Yoshikawa,et al.  Multifingered robot hands: Control for grasping and manipulation , 2010, Annu. Rev. Control..

[27]  Manuel Graña,et al.  Hierarchically structured systems , 1986 .

[28]  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).

[29]  William B. Thompson,et al.  Disparity Analysis of Images , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[30]  Alexander Zelinsky,et al.  Building Human-Friendly Robot Systems , 2000 .

[31]  Rachid Deriche,et al.  Robust Recovery of the Epipolar Geometry for an Uncalibrated Stereo Rig , 1994, ECCV.

[32]  Jeffrey C. Trinkle,et al.  An Investigation of Frictionless Enveloping Grasping in the Plane , 1988, Int. J. Robotics Res..

[33]  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).

[34]  Marc Rioux,et al.  Design Of A Large Depth Of View Three-Dimensional Camera For Robot Vision , 1987 .

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

[36]  Antonio Bicchi,et al.  Hands for dexterous manipulation and robust grasping: a difficult road toward simplicity , 2000, IEEE Trans. Robotics Autom..