# Hierarchical Robot Navigation in Novel Environments using Rough 2-D Maps

arXiv: https://arxiv.org/abs/2106.03665 · HTML: https://mlsanity.com/paper/2106.03665/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 25 references that are in the map.
- First posted on arXiv 7 Jun 2021.

## Builds on (most cited first, 14 of 14)

- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [World Models](https://mlsanity.com/paper/1803.10122/) (2018)
- [Hindsight Experience Replay](https://mlsanity.com/paper/1707.01495/) (2017)
- [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://mlsanity.com/paper/1502.00956/) (2015)
- [Semi-parametric Topological Memory for Navigation](https://mlsanity.com/paper/1803.00653/) (2018)
- [Search on the Replay Buffer: Bridging Planning and Reinforcement Learning](https://mlsanity.com/paper/1906.05253/) (2019)
- [Planning with Goal-Conditioned Policies](https://mlsanity.com/paper/1911.08453/) (2019)
- [Temporal Difference Models: Model-Free Deep RL for Model-Based Control](https://mlsanity.com/paper/1802.09081/) (2018)
- [An Uncertain Future: Forecasting from Static Images using Variational Autoencoders](https://mlsanity.com/paper/1606.07873/) (2016)
- [Mapping State Space using Landmarks for Universal Goal Reaching](https://mlsanity.com/paper/1908.05451/) (2019)
- [Deep Deterministic Policy Gradient for Urban Traffic Light Control](https://mlsanity.com/paper/1703.09035/) (2017)
- [Hallucinative Topological Memory for Zero-Shot Visual Planning](https://mlsanity.com/paper/2002.12336/) (2020)
- [Intention-Net: Integrating Planning and Deep Learning for Goal-Directed Autonomous Navigation](https://mlsanity.com/paper/1710.05627/) (2017)
- [Teaching a Machine to Read Maps with Deep Reinforcement Learning](https://mlsanity.com/paper/1711.07479/) (2017)

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

- [One-shot Imitation in a Non-Stationary Environment via Multi-Modal Skill](https://mlsanity.com/paper/2402.08369/) (2024)
- [Feudal Networks for Visual Navigation](https://mlsanity.com/paper/2402.12498/) (2024)
- [Asynchronous, Option-Based Multi-Agent Policy Gradient: A Conditional Reasoning Approach](https://mlsanity.com/paper/2203.15925/) (2022)
- [FeudalNav: A Simple Framework for Visual Navigation](https://mlsanity.com/paper/2602.06974/) (2026)

## Abstract (arXiv)

In robot navigation, generalizing quickly to unseen environments is essential. Hierarchical methods inspired by human navigation have been proposed, typically consisting of a high-level landmark proposer and a low-level controller. However, these methods either require precise high-level information to be given in advance or need to construct such guidance from extensive interaction with the environment. In this work, we propose an approach that leverages a rough 2-D map of the environment to navigate in novel environments without requiring further learning. In particular, we introduce a dynamic topological map that can be initialized from the rough 2-D map along with a high-level planning approach for proposing reachable 2-D map patches of the intermediate landmarks between the start and goal locations. To use proposed 2-D patches, we train a deep generative model to generate intermediate landmarks in observation space which are used as subgoals by low-level goal-conditioned reinforcement learning. Importantly, because the low-level controller is only trained with local behaviors (e.g. go across the intersection, turn left at a corner) on existing environments, this framework allows us to generalize to novel environments given only a rough 2-D map, without requiring further learning. Experimental results demonstrate the effectiveness of the proposed framework in both seen and novel environments.