# Integrating Deep Reinforcement and Supervised Learning to Expedite Indoor Mapping

arXiv: https://arxiv.org/abs/2109.08490 · HTML: https://mlsanity.com/paper/2109.08490/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 16 references that are in the map.
- First posted on arXiv 17 Sep 2021.

## Builds on (most cited first, 4 of 4)

- [Agent57: Outperforming the Atari Human Benchmark](https://mlsanity.com/paper/2003.13350/) (2020)
- [Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections](https://mlsanity.com/paper/1606.08921/) (2016)
- [HouseExpo: A Large-scale 2D Indoor Layout Dataset for Learning-based Algorithms on Mobile Robots](https://mlsanity.com/paper/1903.09845/) (2019)
- [Active Robotic Mapping through Deep Reinforcement Learning](https://mlsanity.com/paper/1712.10069/) (2017)

## Built on by (most cited first, 3 of 3)

- [MapEx: Indoor Structure Exploration with Probabilistic Information Gain from Global Map Predictions](https://mlsanity.com/paper/2409.15590/) (2024)
- [Beyond the Frontier: Predicting Unseen Walls from Occupancy Grids by Learning from Floor Plans](https://mlsanity.com/paper/2406.09160/) (2024)
- [MapExRL: Human-Inspired Indoor Exploration with Predicted Environment Context and Reinforcement Learning](https://mlsanity.com/paper/2503.01548/) (2025)

## Abstract (arXiv)

The challenge of mapping indoor environments is addressed. Typical heuristic algorithms for solving the motion planning problem are frontier-based methods, that are especially effective when the environment is completely unknown. However, in cases where prior statistical data on the environment's architectonic features is available, such algorithms can be far from optimal. Furthermore, their calculation time may increase substantially as more areas are exposed. In this paper we propose two means by which to overcome these shortcomings. One is the use of deep reinforcement learning to train the motion planner. The second is the inclusion of a pre-trained generative deep neural network, acting as a map predictor. Each one helps to improve the decision making through use of the learned structural statistics of the environment, and both, being realized as neural networks, ensure a constant calculation time. We show that combining the two methods can shorten the duration of the mapping process by up to 4 times, compared to frontier-based motion planning.