# Deep Reinforcement Learning-Based Semi-Autonomous Control for Magnetic Micro-robot Navigation with Immersive Manipulation

arXiv: https://arxiv.org/abs/2503.06359 · HTML: https://mlsanity.com/paper/2503.06359/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for clinical treatment and surgery, one of 15 directions in Deep reinforcement learning methods.
- Builds on 4 of its 40 references that are in the map.
- First posted on arXiv 8 Mar 2025.

## Placed in

- Deep reinforcement learning methods / [RL for clinical treatment and surgery](https://mlsanity.com/deeprl/rl-for-clinical-treatment-and-surgery/)

## Builds on (most cited first, 4 of 4)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Human-Robot Shared Control for Surgical Robot Based on Context-Aware Sim-to-Real Adaptation](https://mlsanity.com/paper/2204.11116/) (2022)
- [Smart Magnetic Microrobots Learn to Swim with Deep Reinforcement Learning](https://mlsanity.com/paper/2201.05599/) (2022)

## Built on by (most cited first, 1 of 1)

- [Mag-VLA: Vision-Language-Action Model for Bimanual Magnetically Actuated Microrobot Manipulation](https://mlsanity.com/paper/2605.28486/) (2026)

## Abstract (arXiv)

Magnetic micro-robots have demonstrated immense potential in biomedical applications, such as in vivo drug delivery, non-invasive diagnostics, and cell-based therapies, owing to their precise maneuverability and small size. However, current micromanipulation techniques often rely solely on a two-dimensional (2D) microscopic view as sensory feedback, while traditional control interfaces do not provide an intuitive manner for operators to manipulate micro-robots. These limitations increase the cognitive load on operators, who must interpret limited feedback and translate it into effective control actions. To address these challenges, we propose a Deep Reinforcement Learning-Based Semi-Autonomous Control (DRL-SC) framework for magnetic micro-robot navigation in a simulated microvascular system. Our framework integrates Mixed Reality (MR) to facilitate immersive manipulation of micro-robots, thereby enhancing situational awareness and control precision. Simulation and experimental results demonstrate that our approach significantly improves navigation efficiency, reduces control errors, and enhances the overall robustness of the system in simulated microvascular environments.