# Where are the Keys? -- Learning Object-Centric Navigation Policies on Semantic Maps with Graph Convolutional Networks

arXiv: https://arxiv.org/abs/1909.07376 · HTML: https://mlsanity.com/paper/1909.07376/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2019.

## Built on by (most cited first, 4 of 4)

- [Hierarchical Representations and Explicit Memory: Learning Effective Navigation Policies on 3D Scene Graphs using Graph Neural Networks](https://mlsanity.com/paper/2108.01176/) (2021)
- [A modular vision language navigation and manipulation framework for long horizon compositional tasks in indoor environment](https://mlsanity.com/paper/2101.07891/) (2021)
- [Spatial Relation Graph and Graph Convolutional Network for Object Goal Navigation](https://mlsanity.com/paper/2208.13031/) (2022)
- [Learning Embeddings that Capture Spatial Semantics for Indoor Navigation](https://mlsanity.com/paper/2108.00159/) (2021)

## Abstract (arXiv)

Emerging object-based SLAM algorithms can build a graph representation of an environment comprising nodes for robot poses and object landmarks. However, while this map will contain static objects such as furniture or appliances, many moveable objects (e.g. the car keys, the glasses, or a magazine), are not suitable as landmarks and will not be part of the map due to their non-static nature. We show that Graph Convolutional Networks can learn navigation policies to find such unmapped objects by learning to exploit the hidden probabilistic model that governs where these objects appear in the environment. The learned policies can generalise to object classes unseen during training by using word vectors that express semantic similarity as representations for object nodes in the graph. Furthermore, we show that the policies generalise to unseen environments with only minimal loss of performance. We demonstrate that pre-training the policy network with a proxy task can significantly speed up learning, improving sample efficiency.