From Transportation to Manipulation: Transforming Magnetic Levitation to Magnetic Robotics

Magnetic Levitation (MagLev) systems fundamentally increase the flexibility of in-machine material flow in industrial automation. Therefore, these systems enable dynamic throughput optimization, which is especially beneficial for high-mix low-volume manufacturing. Until now, MagLev installations have been used primarily for in-machine transport, while their potential for manipulation is largely unexplored. This paper introduces the 6D-Platform MagBot, a low-cost six degrees of freedom parallel kinematic that couples two movers into a composite robotic platform. Experiments show that the 6D-Platform MagBot achieves sub-millimeter positioning accuracy and supports fully autonomous pick up and drop off via a docking station, allowing rapid and repeatable reconfiguration of the machine. Relative to a single mover, the proposed platform substantially expands the reachable workspace, payload, and functional dexterity. By unifying transportation and manipulation, this work advances Magnetic Levitation towards Magnetic Robotics, enabling manufacturing solutions that are more agile, efficient, and adaptable.

SHaRe-RL: Structured, Interactive Reinforcement Learning for Contact-Rich Industrial Assembly Tasks

High-mix low-volume (HMLV) industrial assembly, common in small and medium-sized enterprises (SMEs), requires the same precision, safety, and reliability as high-volume automation while remaining flexible to product variation and environmental uncertainty. Current robotic systems struggle to meet these demands. Manual programming is brittle and costly to adapt, while learning-based methods suffer from poor sample efficiency and unsafe exploration in contact-rich tasks. To address this, we present SHaRe-RL, a reinforcement learning framework that leverages multiple sources of prior knowledge. By (i) structuring skills into manipulation primitives, (ii) incorporating human demonstrations and online corrections, and (iii) bounding interaction forces with per-axis compliance, SHaRe-RL enables efficient and safe online learning for long-horizon, contact-rich industrial assembly tasks. Experiments on the insertion of industrial Harting connector modules with 0.2-0.4 mm clearance demonstrate that SHaRe-RL achieves reliable performance within practical time budgets. Our results show that process expertise, without requiring robotics or RL knowledge, can meaningfully contribute to learning, enabling safer, more robust, and more economically viable deployment of RL for industrial assembly.

Precision-Focused Reinforcement Learning Model for Robotic Object Pushing

Non-prehensile manipulation, such as pushing objects to a desired target position, is an important skill for robots to assist humans in everyday situations. However, the task is challenging due to the large variety of objects with different and sometimes unknown physical properties, such as shape, size, mass, and friction. This can lead to the object overshooting its target position, requiring fast corrective movements of the robot around the object, especially in cases where objects need to be precisely pushed. In this paper, we improve the state-of-the-art by introducing a new memory-based vision-proprioception RL model to push objects more precisely to target positions using fewer corrective movements.