Affordance Learning Based on Subtask's Optimal Strategy

Abstract

Affordances define the relationships between the robot and environment, in terms of actions that the robot is able to perform. Prior work is mainly about predicting the possibility of a reactive action, and the object's affordance is invariable. However, in the domain of dynamic programming, a robot's task could often be decomposed into several subtasks, and each subtask could limit the search space. As a result, the robot only needs to replan its sub-strategy when an unexpected situation happens, and an object's affordance might change over time depending on the robot's state and current subtask. In this paper, we propose a novel affordance model linking the subtask, object, robot state and optimal action. An affordance represents the first action of the optimal strategy under the current subtask when detecting an object, and its influence is promoted from a primitive action to the subtask strategy. Furthermore, hierarchical reinforcement learning and state abstraction mechanism are introduced to learn the task graph and reduce state space. In the navigation experiment, the robot equipped with a camera could learn the objects' crucial characteristics, and gain their affordances in different subtasks.