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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Vydáno v:IEEE robotics and automation letters Ročník 6; číslo 3; s. 5509 - 5516
Hlavní autoři: Spector, Oren, Castro, Dotan Di
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.07.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Assembly
Cameras
deep learning methods
Force
Hidden Markov models
Insertion
Machine learning for robot control
Robot vision systems
Robots
Task analysis
Task complexity
Visualization
ISSN:2377-3766, 2377-3766
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Shrnutí: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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SourceType-Scholarly Journals-1
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ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2021.3076971