Picking a Conveyor Clean by an Autonomously Learning Robot

TL;DR

This paper introduces an autonomous robot system that learns to improve its object picking capabilities in waste sorting, achieving high success rates with minimal human intervention through machine learning and feedback sensors.

Contribution

The paper presents a novel autonomous picking system that self-calibrates and enhances its performance over time using feedback sensors and machine learning techniques.

Findings

System improves its pick success rate over time

Achieved 70 out of 80 objects correctly sorted

Demonstrated autonomous calibration and learning capabilities

Abstract

We present a research picking prototype related to our company's industrial waste sorting application. The goal of the prototype is to be as autonomous as possible and it both calibrates itself and improves its picking with minimal human intervention. The system learns to pick objects better based on a feedback sensor in its gripper and uses machine learning to choosing the best proposal from a random sample produced by simple hard-coded geometric models. We show experimentally the system improving its picking autonomously by measuring the pick success rate as function of time. We also show how this system can pick a conveyor belt clean, depositing 70 out of 80 objects in a difficult to manipulate pile of novel objects into the correct chute. We discuss potential improvements and next steps in this direction.