# Active Object Perceiver: Recognition-guided Policy Learning for Object Searching on Mobile Robots

arXiv: https://arxiv.org/abs/1807.11174 · HTML: https://mlsanity.com/paper/1807.11174/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jul 2018.

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

- [Eye, Robot: Learning to Look to Act with a BC-RL Perception-Action Loop](https://mlsanity.com/paper/2506.10968/) (2025)
- [Object Memory Transformer for Object Goal Navigation](https://mlsanity.com/paper/2203.14708/) (2022)
- [Towards Target-Driven Visual Navigation in Indoor Scenes via Generative Imitation Learning](https://mlsanity.com/paper/2009.14509/) (2020)
- [Hierarchical and Partially Observable Goal-driven Policy Learning with Goals Relational Graph](https://mlsanity.com/paper/2103.01350/) (2021)
- [GUTS: Generalized Uncertainty-Aware Thompson Sampling for Multi-Agent Active Search](https://mlsanity.com/paper/2304.02075/) (2023)
- [Integrating Deep RL and Bayesian Inference for ObjectNav in Mobile Robotics](https://mlsanity.com/paper/2603.25366/) (2026)
- [EyeRobot 2.0: Active Gaze for Precise Manipulation without Wrist Cameras](https://mlsanity.com/paper/2610.03710/) (2026)

## Abstract (arXiv)

We study the problem of learning a navigation policy for a robot to actively search for an object of interest in an indoor environment solely from its visual inputs. While scene-driven visual navigation has been widely studied, prior efforts on learning navigation policies for robots to find objects are limited. The problem is often more challenging than target scene finding as the target objects can be very small in the view and can be in an arbitrary pose. We approach the problem from an active perceiver perspective, and propose a novel framework that integrates a deep neural network based object recognition module and a deep reinforcement learning based action prediction mechanism. To validate our method, we conduct experiments on both a simulation dataset (AI2-THOR) and a real-world environment with a physical robot. We further propose a new decaying reward function to learn the control policy specific to the object searching task. Experimental results validate the efficacy of our method, which outperforms competing methods in both average trajectory length and success rate.