Object Goal Navigation requires a robot to find and navigate to an instance of a target object class in a previously unseen environment. Our framework incrementally builds a semantic map of the environment over time, and then repeatedly selects a long-term goal ('where to go') based on the semantic map to locate the target object instance. Long-term goal selection is formulated as a vision-based deep reinforcement learning problem. Specifically, an Encoder Network is trained to extract high-level features from a semantic map and select a long-term goal. In addition, we incorporate data augmentation and Q-function regularization to make the long-term goal selection more effective. We report experimental results using the photo-realistic Gibson benchmark dataset in the AI Habitat 3D simulation environment to demonstrate substantial performance improvement on standard measures in comparison with a state of the art data-driven baseline.
@article{arxiv.2208.13009,
title = {Object Goal Navigation using Data Regularized Q-Learning},
author = {Nandiraju Gireesh and D. A. Sasi Kiran and Snehasis Banerjee and Mohan Sridharan and Brojeshwar Bhowmick and Madhava Krishna},
journal= {arXiv preprint arXiv:2208.13009},
year = {2022}
}