# Explore before Moving: A Feasible Path Estimation and Memory Recalling Framework for Embodied Navigation

arXiv: https://arxiv.org/abs/2110.08571 · HTML: https://mlsanity.com/paper/2110.08571/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 37 references that are in the map.
- First posted on arXiv 16 Oct 2021.

## Builds on (most cited first, 23 of 23)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [VQA: Visual Question Answering](https://mlsanity.com/paper/1505.00468/) (2015)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments](https://mlsanity.com/paper/1711.07280/) (2017)
- [Semantic Scene Completion from a Single Depth Image](https://mlsanity.com/paper/1611.08974/) (2016)
- [Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning](https://mlsanity.com/paper/1609.05143/) (2016)
- [Embodied Question Answering](https://mlsanity.com/paper/1711.11543/) (2017)
- [Reinforced Cross-Modal Matching and Self-Supervised Imitation Learning for Vision-Language Navigation](https://mlsanity.com/paper/1811.10092/) (2018)
- [Cognitive Mapping and Planning for Visual Navigation](https://mlsanity.com/paper/1702.03920/) (2017)
- [Self-Monitoring Navigation Agent via Auxiliary Progress Estimation](https://mlsanity.com/paper/1901.03035/) (2019)
- [Reinforcement and Imitation Learning via Interactive No-Regret Learning](https://mlsanity.com/paper/1406.5979/) (2014)
- [Building Generalizable Agents with a Realistic and Rich 3D Environment](https://mlsanity.com/paper/1801.02209/) (2018)
- [The Regretful Agent: Heuristic-Aided Navigation through Progress Estimation](https://mlsanity.com/paper/1903.01602/) (2019)
- [Visual Representations for Semantic Target Driven Navigation](https://mlsanity.com/paper/1805.06066/) (2018)
- [Hierarchical Imitation and Reinforcement Learning](https://mlsanity.com/paper/1803.00590/) (2018)
- [Vision-based Navigation with Language-based Assistance via Imitation Learning with Indirect Intervention](https://mlsanity.com/paper/1812.04155/) (2018)
- [Multi-Target Embodied Question Answering](https://mlsanity.com/paper/1904.04686/) (2019)
- [PRM-RL: Long-range Robotic Navigation Tasks by Combining Reinforcement Learning and Sampling-based Planning](https://mlsanity.com/paper/1710.03937/) (2017)
- [Neural Modular Control for Embodied Question Answering](https://mlsanity.com/paper/1810.11181/) (2018)
- [Blindfold Baselines for Embodied QA](https://mlsanity.com/paper/1811.05013/) (2018)
- [Walking with MIND: Mental Imagery eNhanceD Embodied QA](https://mlsanity.com/paper/1908.01482/) (2019)
- [Revisiting EmbodiedQA: A Simple Baseline and Beyond](https://mlsanity.com/paper/1904.04166/) (2019)
- [On the Complexity of Exploration in Goal-Driven Navigation](https://mlsanity.com/paper/1811.06889/) (2018)

## Abstract (arXiv)

An embodied task such as embodied question answering (EmbodiedQA), requires an agent to explore the environment and collect clues to answer a given question that related with specific objects in the scene. The solution of such task usually includes two stages, a navigator and a visual Q&A module. In this paper, we focus on the navigation and solve the problem of existing navigation algorithms lacking experience and common sense, which essentially results in a failure finding target when robot is spawn in unknown environments. Inspired by the human ability to think twice before moving and conceive several feasible paths to seek a goal in unfamiliar scenes, we present a route planning method named Path Estimation and Memory Recalling (PEMR) framework. PEMR includes a "looking ahead" process, textiti.e. a visual feature extractor module that estimates feasible paths for gathering 3D navigational information, which is mimicking the human sense of direction. PEMR contains another process ``looking behind'' process that is a memory recall mechanism aims at fully leveraging past experience collected by the feature extractor. Last but not the least, to encourage the navigator to learn more accurate prior expert experience, we improve the original benchmark dataset and provide a family of evaluation metrics for diagnosing both navigation and question answering modules. We show strong experimental results of PEMR on the EmbodiedQA navigation task.