# Learning Your Way Without Map or Compass: Panoramic Target Driven Visual Navigation

arXiv: https://arxiv.org/abs/1909.09295 · HTML: https://mlsanity.com/paper/1909.09295/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Placed in Language-conditioned policies and VLAs, one of 13 directions in Offline RL and imitation learning from data.
- First posted on arXiv 20 Sep 2019.

## Placed in

- Offline RL and imitation learning from data / [Language-conditioned policies and VLAs](https://mlsanity.com/offlinerl/language-conditioned-policies-and-vlas/)

## Built on by (most cited first, 7 of 7)

- [The Surprising Effectiveness of Visual Odometry Techniques for Embodied PointGoal Navigation](https://mlsanity.com/paper/2108.11550/) (2021)
- [Teaching Agents how to Map: Spatial Reasoning for Multi-Object Navigation](https://mlsanity.com/paper/2107.06011/) (2021)
- [Towards Target-Driven Visual Navigation in Indoor Scenes via Generative Imitation Learning](https://mlsanity.com/paper/2009.14509/) (2020)
- [Combining Learning from Human Feedback and Knowledge Engineering to Solve Hierarchical Tasks in Minecraft](https://mlsanity.com/paper/2112.03482/) (2021)
- [An A* Curriculum Approach to Reinforcement Learning for RGBD Indoor Robot Navigation](https://mlsanity.com/paper/2101.01774/) (2021)
- [Private Multiparty Perception for Navigation](https://mlsanity.com/paper/2212.00912/) (2022)
- [Landmark Policy Optimization for Object Navigation Task](https://mlsanity.com/paper/2109.09512/) (2021)

## Abstract (arXiv)

We present a robot navigation system that uses an imitation learning framework to successfully navigate in complex environments. Our framework takes a pre-built 3D scan of a real environment and trains an agent from pre-generated expert trajectories to navigate to any position given a panoramic view of the goal and the current visual input without relying on map, compass, odometry, or relative position of the target at runtime. Our end-to-end trained agent uses RGB and depth (RGBD) information and can handle large environments (up to 1031m2) across multiple rooms (up to 40) and generalizes to unseen targets. We show that when compared to several baselines our method (1) requires fewer training examples and less training time, (2) reaches the goal location with higher accuracy, and (3) produces better solutions with shorter paths for long-range navigation tasks.