Self-driving laboratories have recently emerged as a transformative research paradigm that integrates automated experimental systems with AI-based decision-making to significantly accelerate material discovery. While prior efforts have focused primarily on automating nanoparticle synthesis, achieving true experimental autonomy necessitates the inclusion of postsynthesis preprocessing steps such as washing, separation, and purification. Among these methods, the nanoparticle washing process presents a unique automation challenge. Despite its apparent simplicity, the process is inherently adaptive, with experimental conditions and outcomes that vary dynamically in response to subtle changes. Effective automation of nanoparticle washing requires both visual adaptivity—to detect changes in the appearance of dispersions or precipitates—and cognitive adaptivity—to handle failure cases, such as incomplete sedimentation or phase separation. This study introduces an integrated platform that automates this complex preprocessing task, significantly broadening the operational capabilities of current self-driving labs. The proposed platform offers a practical and scalable solution for realizing fully autonomous experimental workflows. By bridging the gap between synthesis and characterization through intelligent preprocessing, this work represents a critical step toward establishing an end-to-end autonomous pipeline for materials development.