InsertionNet - A Scalable Solution for Insertion
Complicated assembly processes can be described as a sequence of two main activities: grasping and insertion. While general grasping solutions are common in industry, insertion is still only applicable to small subsets of problems, mainly ones involving simple shapes in fixed locations and in which...
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| Published in: | IEEE robotics and automation letters Vol. 6; no. 3; pp. 5509 - 5516 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
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Piscataway
IEEE
01.07.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 2377-3766, 2377-3766 |
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| Abstract | Complicated assembly processes can be described as a sequence of two main activities: grasping and insertion. While general grasping solutions are common in industry, insertion is still only applicable to small subsets of problems, mainly ones involving simple shapes in fixed locations and in which the variations are not taken into consideration. Recently, RL approaches with prior knowledge (e.g., LfD or residual policy) have been adopted. However, these approaches might be problematic in contact-rich tasks since interaction might endanger the robot and its equipment. In this letter, we tackled this challenge by formulating the problem as a regression problem. By combining visual and force inputs, we demonstrate that our method can scale to 16 different insertion tasks in less than 10 minutes. The resulting policies are robust to changes in the socket position, orientation or peg color, as well as to small differences in peg shape. Finally, we demonstrate an end-to-end solution for 2 complex assembly tasks with multi-insertion objectives when the assembly board is randomly placed on a table. |
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| AbstractList | Complicated assembly processes can be described as a sequence of two main activities: grasping and insertion. While general grasping solutions are common in industry, insertion is still only applicable to small subsets of problems, mainly ones involving simple shapes in fixed locations and in which the variations are not taken into consideration. Recently, RL approaches with prior knowledge (e.g., LfD or residual policy) have been adopted. However, these approaches might be problematic in contact-rich tasks since interaction might endanger the robot and its equipment. In this letter, we tackled this challenge by formulating the problem as a regression problem. By combining visual and force inputs, we demonstrate that our method can scale to 16 different insertion tasks in less than 10 minutes. The resulting policies are robust to changes in the socket position, orientation or peg color, as well as to small differences in peg shape. Finally, we demonstrate an end-to-end solution for 2 complex assembly tasks with multi-insertion objectives when the assembly board is randomly placed on a table. |
| Author | Spector, Oren Castro, Dotan Di |
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| References | ref12 davchev (ref16) 2020 ref11 spector (ref5) 2020 ref2 ref1 s (ref10) 0 ref17 ref19 ref18 murphy (ref9) 2012 silver (ref20) 2018 ref24 lee (ref22) 0 ref26 ref25 schoettler (ref15) 2019 xu (ref8) 2019 eigen (ref23) 0 kalashnikov (ref14) 2018 ref21 ref7 ref4 ref3 ref6 sutton (ref13) 2018 |
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| SubjectTerms | Assembly Cameras deep learning methods Force Hidden Markov models Insertion Machine learning for robot control Robot vision systems Robots Task analysis Task complexity Visualization |
| Title | InsertionNet - A Scalable Solution for Insertion |
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