mlsanityData to 14 Oct 2026

MagBotSim: Physics-Based Simulation and Reinforcement Learning Environments for Magnetic Robotics

arXiv 2511.16158 ↗First posted 20 Nov 2025cs.RO cs.LGcited by 0

On the map: Deep reinforcement learning methods → RL for robot locomotion and manipulation

Abstract (arXiv)

Magnetic levitation is about to revolutionize in-machine material flow in industrial automation. Such systems are flexibly configurable and can include a large number of independently actuated shuttles (movers) that dynamically rebalance production capacity. Beyond their capabilities for dynamic transportation, these systems possess the inherent yet unexploited potential to perform manipulation. By merging the fields of transportation and manipulation into a coordinated swarm of magnetic robots (MagBots), we enable manufacturing systems to achieve significantly higher efficiency, adaptability, and compactness. To support the development of intelligent algorithms for magnetic levitation systems, we introduce MagBotSim (Magnetic Robotics Simulation): a physics-based simulation for magnetic levitation systems. By framing magnetic levitation systems as robot swarms and providing a dedicated simulation, this work lays the foundation for next generation manufacturing systems powered by Magnetic Robotics. MagBotSim's documentation, videos, experiments, and code are available at: https://ubi-coro.github.io/MagBotSim/

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods. Builds on 26 of its 50 references that are in the map. First posted on arXiv 20 Nov 2025.

Builds on

26 of 50 references.

Closest ancestors · 2

Same direction, newest first

  1. 2025SHaRe-RL: Structured, Interactive Reinforcement Learning for Contact-Rich Industrial Assembly Tasks17 Sep
  2. 2024Precision-Focused Reinforcement Learning Model for Robotic Object Pushing13 Nov

Other references · 24

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  2. 2016OpenAI Gymcited by 1,736
  3. 2020D4RL: Datasets for Deep Data-Driven Reinforcement Learningcited by 1,427
  4. 2017Hindsight Experience Replaycited by 1,136
  5. 2021Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learningcited by 998
  6. 2017Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrationscited by 715
  7. 2019Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and Reinforcement Learningcited by 384
  8. 2023Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Taskscited by 327
  9. 2018Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Researchcited by 260
  10. 2020PettingZoo: Gym for Multi-Agent Reinforcement Learningcited by 220
  11. 2020FACMAC: Factored Multi-Agent Centralised Policy Gradientscited by 178
  12. 2023Safety-Gymnasium: A Unified Safe Reinforcement Learning Benchmarkcited by 143
12 more
  1. 2018Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement Learningcited by 128
  2. 2021Learning to Fly -- a Gym Environment with PyBullet Physics for Reinforcement Learning of Multi-agent Quadcopter Controlcited by 77
  3. 2023TorchRL: A data-driven decision-making library for PyTorchcited by 60
  4. 2021panda-gym: Open-source goal-conditioned environments for robotic learningcited by 59
  5. 2021safe-control-gym: a Unified Benchmark Suite for Safe Learning-based Control and Reinforcement Learning in Roboticscited by 50
  6. 2023Keep it Upright: Model Predictive Control for Nonprehensile Object Transportation with Obstacle Avoidance on a Mobile Manipulatorcited by 5
  7. 2025Can Context Bridge the Reality Gap? Sim-to-Real Transfer of Context-Aware Policiescited by 4
  8. 2023MultiVehicle Simulator (MVSim): lightweight dynamics simulator for multiagents and mobile robotics researchcited by 2
  9. 2024Learning Goal-Directed Object Pushing in Cluttered Scenes With Location-Based Attentioncited by 2
  10. 2025End-to-End Low-Level Neural Control of an Industrial-Grade 6D Magnetic Levitation Systemcited by 2
  11. 2022A Solution to Slosh-free Robot Trajectory Optimizationcited by 1
  12. 2024Trajectory Planning and Control for Robotic Magnetic Manipulationcited by 1

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2025Maglev-Pentabot: Magnetic Levitation System for Non-Contact Manipulation using Deep Reinforcement Learning
  2. 2026MicroPush: A Lightweight Simulator for Sim-to-Real Microrobotic Pushing and Assembly ↗
  3. 2026RoboTwin-Phys: Do WAMs and VLAs Understand the Physical World? ↗
  4. 2025MOSAIC: Skill-Centric Manipulation Planning with Physics Simulation
  5. 2024UBSoft: A Simulation Platform for Robotic Skill Learning in Unbounded Soft Environments
  6. 2024Agile Robotics: Optimal Control, Reinforcement Learning, and Differentiable Simulation
  7. 2025Differentiable Skill Optimisation for Powder Manipulation in Laboratory Automation ↗
  8. 2026Optimal Derivative Feedback Control for an Active Magnetic Levitation System: An Experimental Study on Data-Driven Approaches