ArticleNature communications2026
Learning for micro-scale mechano-flow co-manipulation.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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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Authors and funding
5 authors.
Funding
Abstract
Dexterous manipulation is a fundamental yet unresolved topic in robotics, particularly in the realm of micro-manipulation of flexible objects. The complex interplay among external forces, intrinsic material properties, and environmental factors that govern shape deformation presents significant challenges to learning and control. This paper introduces a mechano-flow co-manipulation scheme for flexible micro-objects. It achieves precise handling of floating micro-objects by dynamically adjusting the flow field at the air-liquid interface, enabling simultaneous control of both manipulation and focal planes. A real-time flow prediction framework is proposed based on a reversible neural network, termed FlowNet, achieving bidirectional prediction of input flow and resulting spatio-temporal flow distribution with high accuracy. Through experimental validation, including linear-drive characterization, open-loop trajectory following, and manipulation of different flexible objects, we demonstrate the system's versatility, precision, and stability. The proposed system offers a promising approach for micro-manipulation through mechano-flow interaction for flexible micro-objects.
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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.