用深度强化学习实现克级物体非接触操控，突破微米级限制
Maglev-Pentabot: Magnetic Levitation System for Non-Contact Manipulation using Deep Reinforcement Learning
- 基于数值优化的电磁阵列设计，扩大可控空间
- 提出动作重映射缓解磁场非线性导致的样本稀疏问题
- 可泛化至未训练任务，适合工业级机器人参考
非接触操控在多个工业领域正成为变革性技术。然而，当前灵活的二维和三维非接触操控方法通常局限于微观尺度，一般仅能控制毫克级物体。本文提出一种磁悬浮系统Maglev-Pentabot，旨在解决这一局限。该系统利用深度强化学习（DRL）开发复杂控制策略，实现克级物体的操控。具体而言，我们通过数值分析优化电磁铁布局以最大化可控空间；同时引入动作重映射方法，解决因磁场强度强非线性导致的样本稀疏问题，从而促进DRL控制器收敛。实验结果表明，系统具备灵活操控能力，尤其可泛化至未显式训练的搬运任务。此外，通过使用更大电磁铁，该方法可扩展至操控更重物体，为工业级机器人应用提供参考框架。
原文摘要 · Abstract (English)
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.
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