# Contact Skill Imitation Learning for Robot-Independent Assembly Programming

## TL;DR

This paper introduces a data-driven, robot-independent method for learning contact skills from human demonstrations, enabling robots to perform assembly tasks with robustness and ease of programming.

## Contribution

It presents a novel LSTM-based approach to extract contact skills from simulation, allowing non-experts to program force-sensitive assembly tasks independently of robot models.

## Key findings

- Successfully handles part tilting and jamming during assembly
- Robust to localization uncertainty and different robot configurations
- Enables non-experts to program assembly tasks easily

## Abstract

Robotic automation is a key driver for the advancement of technology. The skills of human workers, however, are difficult to program and seem currently unmatched by technical systems. In this work we present a data-driven approach to extract and learn robot-independent contact skills from human demonstrations in simulation environments, using a Long Short Term Memory (LSTM) network. Our model learns to generate error-correcting sequences of forces and torques in task space from object-relative motion, which industrial robots carry out through a Cartesian force control scheme on the real setup. This scheme uses forward dynamics computation of a virtually conditioned twin of the manipulator to solve the inverse kinematics problem. We evaluate our methods with an assembly experiment, in which our algorithm handles part tilting and jamming in order to succeed. The results show that the skill is robust towards localization uncertainty in task space and across different joint configurations of the robot. With our approach, non-experts can easily program force-sensitive assembly tasks in a robot-independent way.