ArticleAdvanced intelligent systems (Weinheim an der Bergstrasse, Germany)2022
Smart Magnetic Microrobots Learn to Swim with Deep Reinforcement Learning.
Article in Advanced intelligent systems (Weinheim an der Bergstrasse, Germany), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Task-Oriented Biomedical Microrobots: Access, Working Environment, Actuation, Body Design, Detection and Control.Micromachines · 2026Review
- Structural Evolution of Biomedical Microrobots: From Rigid to Soft to Rigid-Soft Integrated Systems.Gels (Basel, Switzerland) · 2026Review
- Physics-constrained learning framework for trustworthy microrobot navigation autonomy.Science advances · 2026Article
- Vision-Based Human-Robot Handover System with Reinforcement Learning.Sensors (Basel, Switzerland) · 2026Article
- Embodied Cross-Domain Intelligence in Biomedical Microrobots: A Review.Cyborg and bionic systems (Washington, D.C.) · 2026Review
- MRI-Guided Magnetic Micro/Nanorobots for Precision Neurotherapeutics: From Intelligent Navigation and BBB Penetration to Clinical Translation.International journal of nanomedicine · 2026Review
- AI-Driven Control Strategies for Biomimetic Robotics: Trends, Challenges, and Future Directions.Biomimetics (Basel, Switzerland) · 2025Review
- Chemotactic navigation in robotic swimmers via reset-free hierarchical reinforcement learning.Nature communications · 2025Article
- Terrestrial locomotion of microscopic robots enabled by 3D nanomembranes with nonreciprocal shape morphing.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Model-based reinforcement learning for ultrasound-driven autonomous microrobots.Nature machine intelligence · 2025Article
- AI driven network pharmacology: Multi-scale mechanisms of traditional Chinese medicine from molecular to patient analysis.Computational and structural biotechnology journal · 2025Review
- Reinforcement learning of biomimetic navigation: a model problem for sperm chemotaxis.The European physical journal. E, Soft matter · 2024Article
- Magnetic Microrobots for In Vivo Cargo Delivery: A Review.Micromachines · 2024Review
- Untethered soft actuators for soft standalone robotics.Nature communications · 2024Review
- Recent Advances in Microrobots Powered by Multi-Physics Field for Biomedical and Environmental Applications.Micromachines · 2024Review
- Learning a Tracking Controller for RollingIEEE robotics and automation letters · 2024Article
- Intelligent Navigation of a Magnetic Microrobot with Model-Free Deep Reinforcement Learning in a Real-World Environment.Micromachines · 2024Article
- Learning to cooperate for low-Reynolds-number swimming: a model problem for gait coordination.Scientific reports · 2023Article
- A Survey on Reinforcement Learning Methods in Bionic Underwater Robots.Biomimetics (Basel, Switzerland) · 2023Article
- System integration of magnetic medical microrobots: from design to control.Frontiers in robotics and AI · 2023Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Swimming microrobots are increasingly developed with complex materials and dynamic shapes and are expected to operate in complex environments in which the system dynamics are difficult to model and positional control of the microrobot is not straightforward to achieve. Deep reinforcement learning is a promising method of autonomously developing robust controllers for creating smart microrobots, which can adapt their behavior to operate in uncharacterized environments without the need to model the system dynamics. This article reports the development of a smart helical magnetic hydrogel microrobot that uses the soft actor critic reinforcement learning algorithm to autonomously derive a control policy which allows the microrobot to swim through an uncharacterized biomimetic fluidic environment under control of a time varying magnetic field generated from a three-axis array of electromagnets. The reinforcement learning agent learned successful control policies from both state vector input and raw images, and the control policies learned by the agent recapitulated the behavior of rationally designed controllers based on physical models of helical swimming microrobots. Deep reinforcement learning applied to microrobot control is likely to significantly expand the capabilities of the next generation of microrobots.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.