# Towards Target-Driven Visual Navigation in Indoor Scenes via Generative Imitation Learning

arXiv: https://arxiv.org/abs/2009.14509 · HTML: https://mlsanity.com/paper/2009.14509/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Placed in Language-conditioned policies and VLAs, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 35 of its 56 references that are in the map.
- First posted on arXiv 30 Sep 2020.

## Placed in

- Offline RL and imitation learning from data / [Language-conditioned policies and VLAs](https://mlsanity.com/offlinerl/language-conditioned-policies-and-vlas/)

## Builds on (most cited first, 30 of 35)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Spectral Normalization for Generative Adversarial Networks](https://mlsanity.com/paper/1802.05957/) (2018)
- [Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1606.03476/) (2016)
- [Habitat: A Platform for Embodied AI Research](https://mlsanity.com/paper/1904.01201/) (2019)
- [On Evaluation of Embodied Navigation Agents](https://mlsanity.com/paper/1807.06757/) (2018)
- [End-to-end Driving via Conditional Imitation Learning](https://mlsanity.com/paper/1710.02410/) (2017)
- [Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age](https://mlsanity.com/paper/1606.05830/) (2016)
- [Reinforcement Learning with Unsupervised Auxiliary Tasks](https://mlsanity.com/paper/1611.05397/) (2016)
- [Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning](https://mlsanity.com/paper/1609.05143/) (2016)
- [Cognitive Mapping and Planning for Visual Navigation](https://mlsanity.com/paper/1702.03920/) (2017)
- [Learning to Navigate in Complex Environments](https://mlsanity.com/paper/1611.03673/) (2016)
- [Learning to Poke by Poking: Experiential Learning of Intuitive Physics](https://mlsanity.com/paper/1606.07419/) (2016)
- [Visual Semantic Navigation using Scene Priors](https://mlsanity.com/paper/1810.06543/) (2018)
- [Zero-Shot Visual Imitation](https://mlsanity.com/paper/1804.08606/) (2018)
- [Learning Exploration Policies for Navigation](https://mlsanity.com/paper/1903.01959/) (2019)
- [Building Generalizable Agents with a Realistic and Rich 3D Environment](https://mlsanity.com/paper/1801.02209/) (2018)
- [Learning to Learn How to Learn: Self-Adaptive Visual Navigation Using Meta-Learning](https://mlsanity.com/paper/1812.00971/) (2018)
- [Visual Representations for Semantic Target Driven Navigation](https://mlsanity.com/paper/1805.06066/) (2018)
- [Reinforcement Learning from Imperfect Demonstrations](https://mlsanity.com/paper/1802.05313/) (2018)
- [Learning Navigation Behaviors End-to-End with AutoRL](https://mlsanity.com/paper/1809.10124/) (2018)
- [Deep Reinforcement Learning with Successor Features for Navigation across Similar Environments](https://mlsanity.com/paper/1612.05533/) (2016)
- [Learning and Querying Fast Generative Models for Reinforcement Learning](https://mlsanity.com/paper/1802.03006/) (2018)
- [Reinforced Imitation: Sample Efficient Deep Reinforcement Learning for Map-less Navigation by Leveraging Prior Demonstrations](https://mlsanity.com/paper/1805.07095/) (2018)
- [Robot Navigation in Crowds by Graph Convolutional Networks with Attention Learned from Human Gaze](https://mlsanity.com/paper/1909.10400/) (2019)
- [SplitNet: Sim2Sim and Task2Task Transfer for Embodied Visual Navigation](https://mlsanity.com/paper/1905.07512/) (2019)
- [DenseCAvoid: Real-time Navigation in Dense Crowds using Anticipatory Behaviors](https://mlsanity.com/paper/2002.03038/) (2020)
- [SCORES: Shape Composition with Recursive Substructure Priors](https://mlsanity.com/paper/1809.05398/) (2018)
- [Learning Resilient Behaviors for Navigation Under Uncertainty](https://mlsanity.com/paper/1910.09998/) (2019)
- [CrowdMove: Autonomous Mapless Navigation in Crowded Scenarios](https://mlsanity.com/paper/1807.07870/) (2018)

## Built on by (most cited first, 14 of 14)

- [Prior Does Matter: Visual Navigation via Denoising Diffusion Bridge Models](https://mlsanity.com/paper/2504.10041/) (2025)
- [Teaching Agents how to Map: Spatial Reasoning for Multi-Object Navigation](https://mlsanity.com/paper/2107.06011/) (2021)
- [MonoDream: Monocular Vision-Language Navigation with Panoramic Dreaming](https://mlsanity.com/paper/2508.02549/) (2025)
- [Vision-based Learning for Drones: A Survey](https://mlsanity.com/paper/2312.05019/) (2023)
- [Multi-Agent Embodied Visual Semantic Navigation with Scene Prior Knowledge](https://mlsanity.com/paper/2109.09531/) (2021)
- [Collaborative Target Search with a Visual Drone Swarm: An Adaptive Curriculum Embedded Multistage Reinforcement Learning Approach](https://mlsanity.com/paper/2204.12181/) (2022)
- [Target Search and Navigation in Heterogeneous Robot Systems with Deep Reinforcement Learning](https://mlsanity.com/paper/2308.00331/) (2023)
- [Visual Hindsight Self-Imitation Learning for Interactive Navigation](https://mlsanity.com/paper/2312.03446/) (2023)
- [textscNaVIDA: Vision-Language Navigation with Inverse Dynamics Augmentation](https://mlsanity.com/paper/2601.18188/) (2026)
- [DiSProD: Differentiable Symbolic Propagation of Distributions for Planning](https://mlsanity.com/paper/2302.01491/) (2023)
- [SplatSearch: Instance Image Goal Navigation for Mobile Robots using 3D Gaussian Splatting and Diffusion Models](https://mlsanity.com/paper/2511.12972/) (2025)
- [Self-Imitated Diffusion Policy for Efficient and Robust Visual Navigation](https://mlsanity.com/paper/2601.22965/) (2026)
- [Collision-Aware Object-Goal Visual Navigation via Two-Stage Deep Reinforcement Learning](https://mlsanity.com/paper/2502.13498/) (2025)
- [UAS Visual Navigation in Large and Unseen Environments via a Meta Agent](https://mlsanity.com/paper/2503.15781/) (2025)

## Abstract (arXiv)

We present a target-driven navigation system to improve mapless visual navigation in indoor scenes. Our method takes a multi-view observation of a robot and a target as inputs at each time step to provide a sequence of actions that move the robot to the target without relying on odometry or GPS at runtime. The system is learned by optimizing a combinational objective encompassing three key designs. First, we propose that an agent conceives the next observation before making an action decision. This is achieved by learning a variational generative module from expert demonstrations. We then propose predicting static collision in advance, as an auxiliary task to improve safety during navigation. Moreover, to alleviate the training data imbalance problem of termination action prediction, we also introduce a target checking module to differentiate from augmenting navigation policy with a termination action. The three proposed designs all contribute to the improved training data efficiency, static collision avoidance, and navigation generalization performance, resulting in a novel target-driven mapless navigation system. Through experiments on a TurtleBot, we provide evidence that our model can be integrated into a robotic system and navigate in the real world. Videos and models can be found in the supplementary material.