The urgent need for renewable energy solutions requires rapid advancements in materials discovery. In response, we present AURORA, an innovative robotic platform that enhances this process by integrating automated synthesis, characterization, and evaluation into a single unit, thereby improving efficiency and reducing errors. Its modular design allows for adaptable screening of diverse materials, including metal halide perovskites, and their application in solar cell devices. Our study demonstrates the ability of AURORA to autonomously synthesize and evaluate polycrystalline, mixed halide perovskites, including a novel mesoscopic solar cell array with improved data reliability and throughput. AURORA also conducts postsynthesis treatments and dynamic analyses under stress, setting it apart from traditional methods. These features make AURORA a transformative tool for the discovery of novel materials, with potential machine learning integration for optimization. Our results highlight the application of AURORA as a robust and adaptable platform for future developments in automated materials research.