# DTG : Diffusion-based Trajectory Generation for Mapless Global Navigation

arXiv: https://arxiv.org/abs/2403.09900 · HTML: https://mlsanity.com/paper/2403.09900/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Placed in Diffusion and flow policies for robotics, one of 14 directions in Diffusion models.
- Builds on 34 of its 71 references that are in the map.
- First posted on arXiv 14 Mar 2024.

## Placed in

- Diffusion models / [Diffusion and flow policies for robotics](https://mlsanity.com/diffusion/diffusion-and-flow-policies-for-robotics/)

## Builds on (most cited first, 30 of 34)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [Variational Diffusion Models](https://mlsanity.com/paper/2107.00630/) (2021)
- [Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks](https://mlsanity.com/paper/1803.10892/) (2018)
- [A Survey of Autonomous Driving: Common Practices and Emerging Technologies](https://mlsanity.com/paper/1906.05113/) (2019)
- [Wayformer: Motion Forecasting via Simple & Efficient Attention Networks](https://mlsanity.com/paper/2207.05844/) (2022)
- [Motion Transformer with Global Intention Localization and Local Movement Refinement](https://mlsanity.com/paper/2209.13508/) (2022)
- [NoMaD: Goal Masked Diffusion Policies for Navigation and Exploration](https://mlsanity.com/paper/2310.07896/) (2023)
- [MultiPath++: Efficient Information Fusion and Trajectory Aggregation for Behavior Prediction](https://mlsanity.com/paper/2111.14973/) (2021)
- [ViNT: A Foundation Model for Visual Navigation](https://mlsanity.com/paper/2306.14846/) (2023)
- [Pointwise Convolutional Neural Networks](https://mlsanity.com/paper/1712.05245/) (2017)
- [An LSTM Network for Highway Trajectory Prediction](https://mlsanity.com/paper/1801.07962/) (2018)
- [Motion Planning and Control for Mobile Robot Navigation Using Machine Learning: a Survey](https://mlsanity.com/paper/2011.13112/) (2020)
- [ViKiNG: Vision-Based Kilometer-Scale Navigation with Geographic Hints](https://mlsanity.com/paper/2202.11271/) (2022)
- [Fast Traversability Estimation for Wild Visual Navigation](https://mlsanity.com/paper/2305.08510/) (2023)
- [WayFAST: Navigation with Predictive Traversability in the Field](https://mlsanity.com/paper/2203.12071/) (2022)
- [LaND: Learning to Navigate from Disengagements](https://mlsanity.com/paper/2010.04689/) (2020)
- [Learning Inverse Kinodynamics for Accurate High-Speed Off-Road Navigation on Unstructured Terrain](https://mlsanity.com/paper/2102.12667/) (2021)
- [GrASPE: Graph based Multimodal Fusion for Robot Navigation in Unstructured Outdoor Environments](https://mlsanity.com/paper/2209.05722/) (2022)
- [DualSMC: Tunneling Differentiable Filtering and Planning under Continuous POMDPs](https://mlsanity.com/paper/1909.13003/) (2019)
- [VI-IKD: High-Speed Accurate Off-Road Navigation using Learned Visual-Inertial Inverse Kinodynamics](https://mlsanity.com/paper/2203.15983/) (2022)
- [Benchmarking Reinforcement Learning Techniques for Autonomous Navigation](https://mlsanity.com/paper/2210.04839/) (2022)
- [ExAug: Robot-Conditioned Navigation Policies via Geometric Experience Augmentation](https://mlsanity.com/paper/2210.07450/) (2022)
- [Toward Wheeled Mobility on Vertically Challenging Terrain: Platforms, Datasets, and Algorithms](https://mlsanity.com/paper/2303.00998/) (2023)
- [Visual Representation Learning for Preference-Aware Path Planning](https://mlsanity.com/paper/2109.08968/) (2021)
- [APPLR: Adaptive Planner Parameter Learning from Reinforcement](https://mlsanity.com/paper/2011.00397/) (2020)
- [STEP: Stochastic Traversability Evaluation and Planning for Risk-Aware Off-road Navigation; Results from the DARPA Subterranean Challenge](https://mlsanity.com/paper/2303.01614/) (2023)
- [Image-Goal Navigation in Complex Environments via Modular Learning](https://mlsanity.com/paper/2108.06105/) (2021)
- [MTG: Mapless Trajectory Generator with Traversability Coverage for Outdoor Navigation](https://mlsanity.com/paper/2309.08214/) (2023)

## Built on by (most cited first, 13 of 13)

- [VLM-Social-Nav: Socially Aware Robot Navigation through Scoring using Vision-Language Models](https://mlsanity.com/paper/2404.00210/) (2024)
- [Rethinking the Embodied Gap in Vision-and-Language Navigation: A Holistic Study of Physical and Visual Disparities](https://mlsanity.com/paper/2507.13019/) (2025)
- [GenDoP: Auto-regressive Camera Trajectory Generation as a Director of Photography](https://mlsanity.com/paper/2504.07083/) (2025)
- [VL-TGS: Trajectory Generation and Selection using Vision Language Models in Mapless Outdoor Environments](https://mlsanity.com/paper/2408.02454/) (2024)
- [Grounded Curriculum Learning](https://mlsanity.com/paper/2409.19816/) (2024)
- [Risk-Guided Diffusion: Toward Deploying Robot Foundation Models in Space, Where Failure Is Not An Option](https://mlsanity.com/paper/2506.17601/) (2025)
- [MetricNet: Recovering Metric Scale in Generative Navigation Policies](https://mlsanity.com/paper/2509.13965/) (2025)
- [VENTURA: Adapting Image Diffusion Models for Unified Task Conditioned Navigation](https://mlsanity.com/paper/2510.01388/) (2025)
- [Deep Active Inference with Diffusion Policy and Multiple Timescale World Model for Real-World Exploration and Navigation](https://mlsanity.com/paper/2510.23258/) (2025)
- [Splatblox: Traversability-Aware Gaussian Splatting for Outdoor Robot Navigation](https://mlsanity.com/paper/2511.18525/) (2025)
- [Multimodal embodiment-aware navigation transformer](https://mlsanity.com/paper/2604.19267/) (2026)
- [AgenticDiffusion: Multi-View Reasoning with View-Conditioned Diffusion Planning for Vision-Based UAV Navigation](https://mlsanity.com/paper/2606.04111/) (2026)
- [Guided Diffusion with Distilled Vision-Language Reliability for Aerial Navigation](https://mlsanity.com/paper/2606.13883/) (2026)

## Abstract (arXiv)

We present a novel end-to-end diffusion-based trajectory generation method, DTG, for mapless global navigation in challenging outdoor scenarios with occlusions and unstructured off-road features like grass, buildings, bushes, etc. Given a distant goal, our approach computes a trajectory that satisfies the following goals: (1) minimize the travel distance to the goal; (2) maximize the traversability by choosing paths that do not lie in undesirable areas. Specifically, we present a novel Conditional RNN(CRNN) for diffusion models to efficiently generate trajectories. Furthermore, we propose an adaptive training method that ensures that the diffusion model generates more traversable trajectories. We evaluate our methods in various outdoor scenes and compare the performance with other global navigation algorithms on a Husky robot. In practice, we observe at least a 15% improvement in traveling distance and around a 7% improvement in traversability.