Robotic Cell Manipulation at the Solid-Liquid Interface for Cryopreservation

Abstract

Automating cell manipulation at a solid-liquid interface is a critical challenge for biomedical applications such as embryo cryopreservation. Unlike manipulation in a full liquid medium, the cell-substrate contact creates a significant static friction force that is not readily measurable with current sensing or vision technologies. This unpredictability poses a high risk of cell loss, as the cell can transition abruptly from a static to a high-velocity state when the applied hydrodynamic force breaches the friction threshold. Existing methods fail to estimate this hidden friction parameter and cannot anticipate the sudden dynamic shift. To address this challenge, this paper proposes a worst-case predictive control approach with online adversarial parameter estimation (WPC-OAPE). The core innovation is the inference of the static friction barrier from observations made while the cell is still stationary. This estimate then informs a predictive controller that proactively plans against the worst-case scenario, to select the optimal action. The WPC-OAPE scheme was validated in robotic embryo vitrification experiments, where it achieved a 100% success rate with zero cell loss. This performance significantly surpassed open-loop (66.6% success) and standard predictive control (83.3% success) methods, proving its potential for clinical applications.