# Structured Exploration Through Instruction Enhancement for Object Navigation

arXiv: https://arxiv.org/abs/2211.08467 · HTML: https://mlsanity.com/paper/2211.08467/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 22 references that are in the map.
- First posted on arXiv 15 Nov 2022.

## Builds on (most cited first, 19 of 19)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Habitat: A Platform for Embodied AI Research](https://mlsanity.com/paper/1904.01201/) (2019)
- [Habitat 2.0: Training Home Assistants to Rearrange their Habitat](https://mlsanity.com/paper/2106.14405/) (2021)
- [Object Goal Navigation using Goal-Oriented Semantic Exploration](https://mlsanity.com/paper/2007.00643/) (2020)
- [Reinforcement Learning with Unsupervised Auxiliary Tasks](https://mlsanity.com/paper/1611.05397/) (2016)
- [DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames](https://mlsanity.com/paper/1911.00357/) (2019)
- [Cognitive Mapping and Planning for Visual Navigation](https://mlsanity.com/paper/1702.03920/) (2017)
- [Language as an Abstraction for Hierarchical Deep Reinforcement Learning](https://mlsanity.com/paper/1906.07343/) (2019)
- [Grounded Language Learning in a Simulated 3D World](https://mlsanity.com/paper/1706.06551/) (2017)
- [Mapping Instructions and Visual Observations to Actions with Reinforcement Learning](https://mlsanity.com/paper/1704.08795/) (2017)
- [Why Does Hierarchy (Sometimes) Work So Well in Reinforcement Learning?](https://mlsanity.com/paper/1909.10618/) (2019)
- [Auxiliary Tasks Speed Up Learning PointGoal Navigation](https://mlsanity.com/paper/2007.04561/) (2020)
- [AllenAct: A Framework for Embodied AI Research](https://mlsanity.com/paper/2008.12760/) (2020)
- [Seeing the Un-Scene: Learning Amodal Semantic Maps for Room Navigation](https://mlsanity.com/paper/2007.09841/) (2020)
- [Differentiable SLAM-net: Learning Particle SLAM for Visual Navigation](https://mlsanity.com/paper/2105.07593/) (2021)
- [Ask Your Humans: Using Human Instructions to Improve Generalization in Reinforcement Learning](https://mlsanity.com/paper/2011.00517/) (2020)
- [Hierarchical Decision Making by Generating and Following Natural Language Instructions](https://mlsanity.com/paper/1906.00744/) (2019)
- [Benchmarking Classic and Learned Navigation in Complex 3D Environments](https://mlsanity.com/paper/1901.10915/) (2019)

## Abstract (arXiv)

Finding an object of a specific class in an unseen environment remains an unsolved navigation problem. Hence, we propose a hierarchical learning-based method for object navigation. The top-level is capable of high-level planning, and building a memory on a floorplan-level (e.g., which room makes the most sense for the agent to visit next, where has the agent already been?). While the lower-level is tasked with efficiently navigating between rooms and looking for objects in them. Instructions can be provided to the agent using a simple synthetic language. The top-level intelligently enhances the instructions in order to make the overall task more tractable. Language grounding, mapping instructions to visual observations, is performed by utilizing an additional separate supervised trained goal assessment module. We demonstrate the effectiveness of our method on a dynamic configurable domestic environment.