Modeling Object’s Affordances via Reward Functions

Object affordance learning is the ability to process information about objects and how to use them. Embedding this knowledge in robots is an essential step for the development of intelligent and truly autonomous agents. This work proposes the development of a framework for learning affordances for robotic manipulation. The proposed approach was implemented as a reinforcement function in a network with a Soft Actor-Critic (SAC) algorithm and trained in simulation with a humanoid robot. Among different affordance complexities (touching and grabbing the object), the results show a rate of up to 95% correctness in the best scenario, with the agent properly performing all desired actions. Such results suggest that it is possible to define reward functions representing the object’s affordances for different objects.