New Benchmark Measures Memory Capabilities of VLM-Powered Robots

In-home assistive robots might be able to clean and organize your home, but if they can’t tell you where they put your keys or wallet, they can become a major inconvenience. To improve robot performance and advance memory capabilities needed for reliable home use, Georgia Tech researchers—including OMSCS instructor Zsolt Kira—have developed a new benchmark to evaluate the memory capabilities of vision-language models (VLMs).