# Direct learning of home vector direction for insect-inspired robot navigation

arXiv: https://arxiv.org/abs/2405.03827 · HTML: https://mlsanity.com/paper/2405.03827/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 6 May 2024.

## Builds on (most cited first, 5 of 5)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Flightmare: A Flexible Quadrotor Simulator](https://mlsanity.com/paper/2009.00563/) (2020)
- [MeSLAM: Memory Efficient SLAM based on Neural Fields](https://mlsanity.com/paper/2209.09357/) (2022)
- [OmniSCV: An Omnidirectional Synthetic Image Generator for Computer Vision](https://mlsanity.com/paper/2401.17061/) (2024)

## Built on by (most cited first, 1 of 1)

- [Visual Homing in Outdoor Robots Using Mushroom Body Circuits and Learning Walks](https://mlsanity.com/paper/2507.09725/) (2025)

## Abstract (arXiv)

Insects have long been recognized for their ability to navigate and return home using visual cues from their nest's environment. However, the precise mechanism underlying this remarkable homing skill remains a subject of ongoing investigation. Drawing inspiration from the learning flights of honey bees and wasps, we propose a robot navigation method that directly learns the home vector direction from visual percepts during a learning flight in the vicinity of the nest. After learning, the robot will travel away from the nest, come back by means of odometry, and eliminate the resultant drift by inferring the home vector orientation from the currently experienced view. Using a compact convolutional neural network, we demonstrate successful learning in both simulated and real forest environments, as well as successful homing control of a simulated quadrotor. The average errors of the inferred home vectors in general stay well below the 90deg required for successful homing, and below 24deg if all images contain sufficient texture and illumination. Moreover, we show that the trajectory followed during the initial learning flight has a pronounced impact on the network's performance. A higher density of sample points in proximity to the nest results in a more consistent return. Code and data are available at https://mavlab.tudelft.nl/learningtohome .