InsertionNet -- A Scalable Solution for Insertion

TL;DR

This paper introduces InsertionNet, a scalable and robust learning-based method that combines visual and force data to perform multiple insertion tasks efficiently, even with variations and complex assembly scenarios.

Contribution

The paper presents a novel regression-based approach that integrates visual and force inputs, enabling scalable and robust insertion policies for diverse assembly tasks.

Findings

Successfully scaled to 16 insertion tasks in under 10 minutes

Policies are robust to socket and peg variations

Effective in complex multi-insertion assembly scenarios

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 paper, 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.