ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2024
Learning Soft Millirobot Multimodal Locomotion with Sim-to-Real Transfer.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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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
7 citing papers in PubMed.
- A Data-Driven Inverse Design Methodology for Magnetic Soft Millirobots Navigating in Confined Spaces.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A Multimodal Magnetically Driven Soft Robot With Integrated Actuation-Sensing Based on Photothermal Reprogramming Technology.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Convergence of Soft Electronics and Artificial Intelligence: From Materials to Intelligent Systems.Nano-micro letters · 2026Review
- Magnetically controlled multimodal motion for environmentally adaptive soft millirobots with transformable wheel-leg morphology.Innovation (Cambridge (Mass.)) · 2026Article
- A Bio-Inspired Data-Driven Locomotion Optimization Framework for Adaptive Soft Inchworm Robots.Biomimetics (Basel, Switzerland) · 2025Article
- Addressable and perceptible dynamic reprogram of ferromagnetic soft machines.Nature communications · 2025Article
- Learning Soft Millirobot Multimodal Locomotion with Sim-to-Real Transfer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
With wireless multimodal locomotion capabilities, magnetic soft millirobots have emerged as potential minimally invasive medical robotic platforms. Due to their diverse shape programming capability, they can generate various locomotion modes, and their locomotion can be adapted to different environments by controlling the external magnetic field signal. Existing adaptation methods, however, are based on hand-tuned signals. Here, a learning-based adaptive magnetic soft millirobot multimodal locomotion framework empowered by sim-to-real transfer is presented. Developing a data-driven magnetic soft millirobot simulation environment, the periodic magnetic actuation signal is learned for a given soft millirobot in simulation. Then, the learned locomotion strategy is deployed to the real world using Bayesian optimization and Gaussian processes. Finally, automated domain recognition and locomotion adaptation for unknown environments using a Kullback-Leibler divergence-based probabilistic method are illustrated. This method can enable soft millirobot locomotion to quickly and continuously adapt to environmental changes and explore the actuation space for unanticipated solutions with minimum experimental cost.
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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.