<p>➀ MIT developed a "Relevance" system to help robots prioritize tasks using audio-visual inputs and predict human intentions for efficient assistance; </p><p>➁ The system achieved 90% goal prediction accuracy and 96% correct object selection in breakfast scenario testing, with 60% fewer safety incidents; </p><p>➂ Inspired by human brain's RAS mechanism, the robot dynamically switches between observation mode and active assistance mode based on environmental context recognition.</p>