VERSES multi-agent robotics model works without pre-training - The Robot Report

VERSES AI Inc. has developed a novel multi-agent robotics architecture based on hierarchical active inference that enables robots to perform typical household tasks more effectively than existing models without requiring any pre-training. Unlike traditional robotics approaches—drive-by-wire systems that rely on pre-programming and deep learning models that need extensive training data—VERSES’ system adapts dynamically by exploring its environment, using integrated vision, planning, and control modules. This approach allows robots to handle unexpected obstacles and changes in their surroundings, overcoming common limitations such as freezing or halting when encountering unfamiliar situations. The company, founded in 2020 and based in Vancouver, emphasizes that its platform is inspired by principles from science, physics, and biology to generate reliable predictions and decisions under uncertainty. In comparative tests involving household tasks like tidying a room, preparing groceries, and setting a table, the VERSES model achieved a 66.5% success rate, outperforming a deep learning baseline that scored 54.7%. VERSES claims this