<ul><li>Researchers from Google DeepMind propose RT-Affordance, a hierarchical method that uses affordances as an intermediate representation for policies.</li><li>RT-Affordance integrates vision, language, and action-based decision-making to guide robots in various tasks.</li><li>It improves the robustness and generalization of robot policies, surpassing traditional methods in terms of performance.</li><li>RT-Affordance shows promising results in tasks like robotic grasping and object placement, but has limitations when faced with entirely new objects.</li></ul>

Google DeepMind Researchers Propose RT-Affordance: A Hierarchical Method that Uses Affordances as an Intermediate Representation for Policies

Discover more