On-policy Object Goal Navigation with Exploration Bonuses

Abstract

Machine learning developments have contributed to overcome a wide range of issues, including robotic motion, autonomous navigation, and natural language processing. Of note are the advancements of reinforcement learning in the area of object goal navigation — the task of autonomously traveling to target objects with minimal a priori knowledge of the environment. Given the sparse placement of goals in unknown scenes, exploration is essential for reaching remote objects of interest that are not immediately visible to autonomous agents. Sparse rewards are a crucial problem in reinforcement learning that arises in object goal navigation, as positive rewards are only attained when targets are found at the end of an agent’s trajectory. As such, this work explores object goal navigation and the challenges it presents, along with the relevant reinforcement learning techniques applied to the task. An ablation study of the baseline approach for the RoboTHOR 2021 object goal navigation challenge is presented and used to guide the development of an on-policy agent that is computationally less expensive and obtains greater success in unseen environments. Then, original object goal navigation reward schemes that aggregate episodic and long-term novelty bonuses are proposed, and obtain success rates comparable to the respective object goal navigation benchmark at a fraction of training interactions with the environment.