ArticleNature machine intelligence2025
Model-based reinforcement learning for ultrasound-driven autonomous microrobots.
Article in Nature machine intelligence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Advancements in Machine Learning-Assisted Flexible Electronics: Technologies, Applications, and Future Prospects.Biosensors · 2026Pooled it
- Magnetic nanorobots in oncology: From targeted drug delivery to smart theranostic systems.Materials today. Bio · 2026Review
- Field-Driven Active Colloids as Distributed Micromachines: Bridging Physics, Control, and Function Across Length Scales.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Sensor-Driven World Models for Embodied Intelligence: A Survey of Sensor-State-Decision Modeling Across Far-Field and Near-Field Regimes.Sensors (Basel, Switzerland) · 2026Article
- Selective Class-Aware Refinement (SCAR) Method for Microrobot Detection in Ultrasound Images.Sensors (Basel, Switzerland) · 2026Article
- Micro/Nanorobots (MNRs) at the Intersection of Mechanobiology and Artificial Intelligence: An Emerging Frontier for Precision Nanomedicine.ACS nano medicine · 2026Review
- Task-Oriented Biomedical Microrobots: Access, Working Environment, Actuation, Body Design, Detection and Control.Micromachines · 2026Review
- Applications of Reinforcement Learning for Autonomous Surgical Robotics: A Systematic Review.Biomimetics (Basel, Switzerland) · 2026Review
- Physics-constrained learning framework for trustworthy microrobot navigation autonomy.Science advances · 2026Article
- Ultrasound-guided closed-loop control of magnetic hydrogel microrobots with adaptive gait switching.Microsystems & nanoengineering · 2026Article
- Reinforcement learning in linear embedding space unlocks generalizable control across soft robot configurations.Nature communications · 2026Article
- Machine learning-driven design of engineered cilia enables hybrid operations in acoustic microrobots.Nature communications · 2026Article
- Artificial intelligence-driven nano-enhanced stem cell therapy for neurodegenerative diseases: from rational design to clinical translation.Journal of nanobiotechnology · 2026Review
- Autonomous Microrobots for Spatiotemporally Active Therapeutic Delivery and Controlled Release.Cyborg and bionic systems (Washington, D.C.) · 2026Review
- Embodied Cross-Domain Intelligence in Biomedical Microrobots: A Review.Cyborg and bionic systems (Washington, D.C.) · 2026Review
- Magnetic Microrobots for Drug Delivery: A Review of Fabrication Materials, Structure Designs and Drug Delivery Strategies.Molecules (Basel, Switzerland) · 2025Review
- Secure Angle-Based Geometric Elimination (SAGE) for Microrobot Path Planning.Micromachines · 2025Article
- Ultrasound-driven programmable artificial muscles.Nature · 2025Article
- Model-based reinforcement learning for ultrasound-driven autonomous microrobots.Nature machine intelligence · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Reinforcement learning is emerging as a powerful tool for microrobots control, as it enables autonomous navigation in environments where classical control approaches fall short. However, applying reinforcement learning to microrobotics is difficult due to the need for large training datasets, the slow convergence in physical systems and poor generalizability across environments. These challenges are amplified in ultrasound-actuated microrobots, which require rapid, precise adjustments in high-dimensional action space, which are often too complex for human operators. Addressing these challenges requires sample-efficient algorithms that adapt from limited data while managing complex physical interactions. To meet these challenges, we implemented model-based reinforcement learning for autonomous control of an ultrasound-driven microrobot, which learns from recurrent imagined environments. Our non-invasive, AI-controlled microrobot offers precise propulsion and efficiently learns from images in data-scarce environments. On transitioning from a pretrained simulation environment, we achieved sample-efficient collision avoidance and channel navigation, reaching a 90% success rate in target navigation across various channels within an hour of fine-tuning. Moreover, our model initially generalized successfully in 50% of tasks in new environments, improving to over 90% with 30 min of further training. We further demonstrated real-time manipulation of microrobots in complex vasculatures under both static and flow conditions, thus underscoring the potential of AI to revolutionize microrobotics in biomedical applications.
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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.