End-to-End Low-Level Neural Control of an Industrial-Grade 6D Magnetic Levitation System

• URL: http://arxiv.org/abs/2509.01388v1
• Date: Mon, 01 Sep 2025 11:33:30 GMT
• Title: End-to-End Low-Level Neural Control of an Industrial-Grade 6D Magnetic Levitation System
• Abstract summary: This paper presents the first neural controller for 6D magnetic levitation.<n>Trained end-to-end on interaction data from a proprietary controller, it directly maps raw sensor data and 6D reference poses to coil current commands.<n>Results underscore the practical feasibility of learning-based neural control in complex physical systems.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Magnetic levitation is poised to revolutionize industrial automation by integrating flexible in-machine product transport and seamless manipulation. It is expected to become the standard drive for automated manufacturing. However, controlling such systems is inherently challenging due to their complex, unstable dynamics. Traditional control approaches, which rely on hand-crafted control engineering, typically yield robust but conservative solutions, with their performance closely tied to the expertise of the engineering team. In contrast, neural control learning presents a promising alternative. This paper presents the first neural controller for 6D magnetic levitation. Trained end-to-end on interaction data from a proprietary controller, it directly maps raw sensor data and 6D reference poses to coil current commands. The neural controller can effectively generalize to previously unseen situations while maintaining accurate and robust control. These results underscore the practical feasibility of learning-based neural control in complex physical systems and suggest a future where such a paradigm could enhance or even substitute traditional engineering approaches in demanding real-world applications. The trained neural controller, source code, and demonstration videos are publicly available at https://sites.google.com/view/neural-maglev.

Related papers

• T-Rex: Tactile-Reactive Dexterous Manipulation [139.71263755530654]
  We introduce a variable-rate Mixture-of-Transformers (MoT) architecture equipped with a novel temporal tactile VQ-VAE encoder.<n>We demonstrate the effectiveness of tactile-reactive policies on 12 manipulation tasks requiring delicate force control and deformable object manipulation.
  arXiv  Detail & Related papers  (2026-06-15T17:59:55Z)
• Imitating What Works: Simulation-Filtered Modular Policy Learning from Human Videos [56.510263910611684]
  We tackle prehensile manipulation, in which tasks involve grasping an object before performing various post-grasp motions.<n>Human videos offer strong signals for learning the post-grasp motions, but they are less useful for learning the prerequisite grasping behaviors.<n>We present Perceive-Simulate-Imitate (PSI), a framework for training a modular manipulation policy using human video motion data.
  arXiv  Detail & Related papers  (2026-02-13T18:59:10Z)
• Maglev-Pentabot: Magnetic Levitation System for Non-Contact Manipulation using Deep Reinforcement Learning [6.530215277181797]
  We present a magnetic levitation system, termed Maglev-Pentabot, designed to address non-contact manipulation limitations.<n>The Maglev-Pentabot leverages deep reinforcement learning to develop complex control strategies for manipulating objects in the gram range.<n>Our system can be scaled to manipulate heavier objects using larger electromagnets, offering a reference framework for industrial-scale robotic applications.
  arXiv  Detail & Related papers  (2025-11-26T08:10:59Z)
• Stabilizing Humanoid Robot Trajectory Generation via Physics-Informed Learning and Control-Informed Steering [10.372548166988127]
  Recent trends in humanoid robot control have successfully employed imitation learning to enable the learned generation of smooth, human-like trajectories from human data.<n>We propose a two-pronged learning strategy which leverages the known physics of the system and fundamental control principles.<n>We validate our method on various locomotion behaviors for the ergoCub humanoid robot, where a physics-informed loss encourages zero contact foot velocity.
  arXiv  Detail & Related papers  (2025-09-29T12:31:14Z)
• Embodied Neuromorphic Control Applied on a 7-DOF Robotic Manipulator [10.642836177302533]
  Inverse dynamics is a fundamental robotics problem, which maps from joint space to torque space of robotic systems.<n>We use Spiking Neural Network to leverage continuity of the motion data to improve control accuracy and eliminate tuning parameters.<n>This work advances embodied neuromorphic control by one step forward from proof of concept to applications in complex real-world tasks.
  arXiv  Detail & Related papers  (2025-04-17T07:13:37Z)
• Feedforward Controllers from Learned Dynamic Local Model Networks with Application to Excavator Assistance Functions [4.664767161598515]
  We provide a criterion for when feedback linearization of LMNs with zero dynamics yields a valid controller. In our experiments, incorporating disturbance signals and multiple inputs and outputs enhances tracking performance of the learned controller.
  arXiv  Detail & Related papers  (2024-09-25T12:40:07Z)
• LLM4Drive: A Survey of Large Language Models for Autonomous Driving [62.10344445241105]
  Large language models (LLMs) have demonstrated abilities including understanding context, logical reasoning, and generating answers. In this paper, we systematically review a research line about textitLarge Language Models for Autonomous Driving (LLM4AD).
  arXiv  Detail & Related papers  (2023-11-02T07:23:33Z)
• Model-free tracking control of complex dynamical trajectories with machine learning [0.2356141385409842]
  We develop a model-free, machine-learning framework to control a two-arm robotic manipulator. We demonstrate the effectiveness of the control framework using a variety of periodic and chaotic signals.
  arXiv  Detail & Related papers  (2023-09-20T17:10:10Z)
• The least-control principle for learning at equilibrium [65.2998274413952]
  We present a new principle for learning equilibrium recurrent neural networks, deep equilibrium models, or meta-learning. Our results shed light on how the brain might learn and offer new ways of approaching a broad class of machine learning problems.
  arXiv  Detail & Related papers  (2022-07-04T11:27:08Z)
• A Barrier Certificate-based Simplex Architecture for Systems with Approximate and Hybrid Dynamics [3.254402439041394]
  Bb-Simplex is a new design for runtime assurance of continuous dynamical systems. Barrier certificates are used to prove that the baseline controller ensures safety. Bb-Simplex can automatically derive switching conditions for complex continuous-time and hybrid systems.
  arXiv  Detail & Related papers  (2022-02-20T02:08:10Z)
• Online-Learning Deep Neuro-Adaptive Dynamic Inversion Controller for Model Free Control [1.3764085113103217]
  A neuro-adaptive controller is implemented featuring a deep neural network trained on a new weight update law. The controller is able to learn the nonlinear plant quickly and displays good performance in the tracking control problem.
  arXiv  Detail & Related papers  (2021-07-21T22:46:03Z)
• Neural Dynamic Policies for End-to-End Sensorimotor Learning [51.24542903398335]
  The current dominant paradigm in sensorimotor control, whether imitation or reinforcement learning, is to train policies directly in raw action spaces. We propose Neural Dynamic Policies (NDPs) that make predictions in trajectory distribution space. NDPs outperform the prior state-of-the-art in terms of either efficiency or performance across several robotic control tasks.
  arXiv  Detail & Related papers  (2020-12-04T18:59:32Z)
• Improving Input-Output Linearizing Controllers for Bipedal Robots via Reinforcement Learning [85.13138591433635]
  The main drawbacks of input-output linearizing controllers are the need for precise dynamics models and not being able to account for input constraints. In this paper, we address both challenges for the specific case of bipedal robot control by the use of reinforcement learning techniques.
  arXiv  Detail & Related papers  (2020-04-15T18:15:49Z)
• Populations of Spiking Neurons for Reservoir Computing: Closed Loop Control of a Compliant Quadruped [64.64924554743982]
  We present a framework for implementing central pattern generators with spiking neural networks to obtain closed loop robot control. We demonstrate the learning of predefined gait patterns, speed control and gait transition on a simulated model of a compliant quadrupedal robot.
  arXiv  Detail & Related papers  (2020-04-09T14:32:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.