Goal-directed affordance prediction at the subtask level

Purpose – This paper aims to present a hybrid control approach that combines learning-based reactive control and affordance-based deliberate control for autonomous mobile robot navigation. Unlike many current navigation approaches which only use learning-based paradigms, the authors focus on how to utilize the machine learning methods for reactive control together with the affordance knowledge that is simultaneously inherent in natural environments to gain advantages from both local and global optimization. Design/methodology/approach – The idea is to decompose the complex and large-scale robot navigation task into multiple sub-tasks and use the hierarchical reinforcement learning (HRL) algorithm, which is well-studied in the learning and control algorithm domains, to decompose the overall task into sub-tasks and learn a grid-topological map of the environment. An affordance-based deliberate controller is used to inspect the affordance knowledge of the obstacles in the environment. The hybrid control architecture is then designed to integrate the learning-based reactive control and affordance-based deliberate control based on the grid-topological and affordance knowledge. Findings – Experiments with computer simulation and an actual humanoid NAO robot have demonstrated the effectiveness of the proposed hybrid approach for mobile robot navigation. Originality/value – The main contributions of this paper are a new robot navigation framework that decomposes a complex navigation task into multiple sub-tasks using the HRL approach, and hybrid control architecture development that integrates learning-based and affordance-based paradigms for autonomous mobile robot navigation.