Robustness of Utilizing Feedback in Embodied Visual Navigation

Abstract

This paper presents a framework for training an agent to actively request help in object-goal navigation tasks, with feedback indicating the location of the target object in its field of view. To make the agent more robust in scenarios where a teacher may not always be available, the proposed training curriculum includes a mix of episodes with and without feedback. The results show that this approach improves the agent's performance, even in the absence of feedback.

Accepted at the ICRA Workshop for Communicating Robot Learning across Human-Robot Interaction