Learning Soft Millirobot Multimodal Locomotion with Sim-to-Real Transfer.

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 u

SUBMITTER: Demir SO 

PROVIDER: S-EPMC11321659 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature