# Attention-based Active Visual Search for Mobile Robots

arXiv: https://arxiv.org/abs/1807.10744 · HTML: https://mlsanity.com/paper/1807.10744/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jul 2018.

## Built on by (most cited first, 8 of 8)

- [Language Understanding for Field and Service Robots in a Priori Unknown Environments](https://mlsanity.com/paper/2105.10396/) (2021)
- [From Psychological Curiosity to Artificial Curiosity: Curiosity-Driven Learning in Artificial Intelligence Tasks](https://mlsanity.com/paper/2201.08300/) (2022)
- [Distracted Robot: How Visual Clutter Undermine Robotic Manipulation](https://mlsanity.com/paper/2511.22780/) (2025)
- [Object-centric proto-symbolic behavioural reasoning from pixels](https://mlsanity.com/paper/2411.17438/) (2024)
- [Active Visual Perception: Opportunities and Challenges](https://mlsanity.com/paper/2512.03687/) (2025)
- [Integrating Deep RL and Bayesian Inference for ObjectNav in Mobile Robotics](https://mlsanity.com/paper/2603.25366/) (2026)
- [Fast Human Attention Prediction for Fixation-guided Active Perception in Autonomous Navigation](https://mlsanity.com/paper/2606.20491/) (2026)
- [EyeRobot 2.0: Active Gaze for Precise Manipulation without Wrist Cameras](https://mlsanity.com/paper/2610.03710/) (2026)

## Abstract (arXiv)

We present an active visual search model for finding objects in unknown environments. The proposed algorithm guides the robot towards the sought object using the relevant stimuli provided by the visual sensors. Existing search strategies are either purely reactive or use simplified sensor models that do not exploit all the visual information available. In this paper, we propose a new model that actively extracts visual information via visual attention techniques and, in conjunction with a non-myopic decision-making algorithm, leads the robot to search more relevant areas of the environment. The attention module couples both top-down and bottom-up attention models enabling the robot to search regions with higher importance first. The proposed algorithm is evaluated on a mobile robot platform in a 3D simulated environment. The results indicate that the use of visual attention significantly improves search, but the degree of improvement depends on the nature of the task and the complexity of the environment. In our experiments, we found that performance enhancements of up to 42% in structured and 38% in highly unstructured cluttered environments can be achieved using visual attention mechanisms.