# GAPLE: Generalizable Approaching Policy LEarning for Robotic Object Searching in Indoor Environment

arXiv: https://arxiv.org/abs/1809.08287 · HTML: https://mlsanity.com/paper/1809.08287/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Sep 2018.

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

- [Hierarchical and Partially Observable Goal-driven Policy Learning with Goals Relational Graph](https://mlsanity.com/paper/2103.01350/) (2021)
- [Goal-Aware Cross-Entropy for Multi-Target Reinforcement Learning](https://mlsanity.com/paper/2110.12985/) (2021)
- [Deep Learning for Embodied Vision Navigation: A Survey](https://mlsanity.com/paper/2108.04097/) (2021)

## Abstract (arXiv)

We study the problem of learning a generalizable action policy for an intelligent agent to actively approach an object of interest in an indoor environment solely from its visual inputs. While scene-driven or recognition-driven visual navigation has been widely studied, prior efforts suffer severely from the limited generalization capability. In this paper, we first argue the object searching task is environment dependent while the approaching ability is general. To learn a generalizable approaching policy, we present a novel solution dubbed as GAPLE which adopts two channels of visual features: depth and semantic segmentation, as the inputs to the policy learning module. The empirical studies conducted on the House3D dataset as well as on a physical platform in a real world scenario validate our hypothesis, and we further provide in-depth qualitative analysis.