Using Neural Networks for Heuristic Grasp Planning in Random Bin Picking

The fast determination of collision-free grasps is a key aspect in random bin picking. Heuristic search algorithms provide a feasible solution to this problem, using statistical data on the likelihood of finding a valid solution on elements with certain parameters. In this paper, we propose the use of several neural networks in such algorithms to accelerate the search while preserving the reliability. This is done by training the neural networks on the heuristic search trees of previous situations and using the output of these neural networks as part of the heuristic function. Finally, the effect of these neural networks is experimentally analyzed with sensor data from a working bin picking system with an industrial dual arm robot and it is shown that the calculation time in this setup is reduced by up to 45%.

[1]  Alexander Verl,et al.  Gripping Point Determination and Collision Prevention in a Bin- Picking application , 2012, ROBOTIK.

[2]  Rolf Dieter Schraft,et al.  Intelligent picking of chaotically stored objects , 2003 .

[3]  Makoto Mizukawa,et al.  Hough-space-based object recognition tightly coupled with path planning for robust and fast bin-picking , 1998, Proceedings. 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems. Innovations in Theory, Practice and Applications (Cat. No.98CH36190).

[4]  Avinash C. Kak,et al.  Model-based vision for robotic manipulation of twisted tubular parts: using affine transforms and heuristic search , 1994, Proceedings of the 1994 IEEE International Conference on Robotics and Automation.

[5]  Andreas Pott,et al.  Statistical analysis of influencing factors for heuristic grip determination in random bin picking , 2017, 2017 IEEE International Conference on Advanced Intelligent Mechatronics (AIM).

[6]  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).

[7]  Andreas Pott,et al.  Object recognition: Bin-picking for industrial use , 2013, IEEE ISR 2013.

[8]  Heinz Wörn,et al.  Path Planning Process Optimization for a Bin Picking System , 2010, ISR/ROBOTIK.

[9]  V. Singule,et al.  3D vision systems for industrial bin-picking applications , 2012, Proceedings of 15th International Conference MECHATRONIKA.

[10]  Andreas Pott,et al.  Gripping Point Determination for Bin Picking Using Heuristic Search , 2017 .

[11]  Simon Winkelbach,et al.  Efficient bin-picking and grasp planning based on depth data , 2013, 2013 IEEE International Conference on Robotics and Automation.

[12]  Sergey Levine,et al.  Unsupervised Learning for Physical Interaction through Video Prediction , 2016, NIPS.

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

[14]  Jyh-Da Wei,et al.  Using Neural Networks for Evaluation in Heuristic Search Algorithm , 2011, AAAI.

[15]  Simon Winkelbach,et al.  RANSAM for Industrial Bin-Picking , 2010, ISR/ROBOTIK.