CAG: Context-Conditional 2D Affordance Generation

Affordance map generation has become a key topic in the field of cognition and decision-making for machines such as robots. Most studies focus on extracting cognition-leveraged action information from human-object interaction videos using affordance networks. Recently, there have been attempts to combine vision and language data in order to improve model versatility. However, previous works cannot capture deep contextual meaning or generate appropriate affordances for different task objectives or situations involving the same object. To address this limitation, we propose Context-conditional 2D Affordance Generation (CAG)—a language-leveraged affordance map generation model. We utilize foundation models to extract contextual knowledge from human video datasets where various objects are interacted with across different environments. Our approach successfully understands given objectives, even when presented with complex sentences, and generates relevant conditional affordance maps.