Afford-X: Generalizable and Slim Affordance Reasoning for Task-oriented Manipulation

Understand

Object affordance reasoning, the ability to infer object functionalities based on physical properties, is fundamental for task-oriented planning and activities in both humans and Artificial Intelligence (AI).

• This capability, required for planning and executing daily activities in a task-oriented manner, relies on commonsense knowledge of object physics and functionalities, extending beyond simple object recognition.
• Current computational models for affordance reasoning from perception lack generalizability, limiting their applicability in novel scenarios.
• Meanwhile, comprehensive Large Language Models (LLMs) with emerging reasoning capabilities are challenging to deploy on local devices for task-oriented manipulations.