Model-based reinforcement learning for ultrasound-driven autonomous microrobots.

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 autonom

SUBMITTER: Medany M 

PROVIDER: S-EPMC12283351 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature