Maglev-Pentabot: Magnetic Levitation System for Non-Contact Manipulation using Deep Reinforcement Learning

TL;DR

This paper introduces Maglev-Pentabot, a magnetic levitation system that uses deep reinforcement learning to enable non-contact manipulation of objects in the gram range, overcoming previous size limitations.

Contribution

The paper presents a novel electromagnet arrangement and an action remapping method to enhance control and generalization in magnetic levitation manipulation using DRL.

Findings

Demonstrates flexible manipulation of objects in the gram range.

Shows generalization to untrained transport tasks.

Scalable approach for heavier objects using larger electromagnets.

Abstract

Non-contact manipulation has emerged as a transformative approach across various industrial fields. However, current flexible 2D and 3D non-contact manipulation techniques are often limited to microscopic scales, typically controlling objects in the milligram range. In this paper, we present a magnetic levitation system, termed Maglev-Pentabot, designed to address this limitation. The Maglev-Pentabot leverages deep reinforcement learning (DRL) to develop complex control strategies for manipulating objects in the gram range. Specifically, we propose an electromagnet arrangement optimized through numerical analysis to maximize controllable space. Additionally, an action remapping method is introduced to address sample sparsity issues caused by the strong nonlinearity in magnetic field intensity, hence allowing the DRL controller to converge. Experimental results demonstrate flexible manipulation capabilities, and notably, our system can generalize to transport tasks it has not been explicitly trained for. Furthermore, our approach can be scaled to manipulate heavier objects using larger electromagnets, offering a reference framework for industrial-scale robotic applications.