Learning for micro-scale mechano-flow co-manipulation

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. Flexible micro-object manipulation is hindered by coupled forces, material properties and environmental effects. The authors report mechano-flow co-manipulation using tunable air-liquid interface flows and FlowNet for real-time bidirectional flow prediction, enabling precise, stable control.