# Dyna-LfLH: Learning Agile Navigation in Dynamic Environments from Learned Hallucination

arXiv: https://arxiv.org/abs/2403.17231 · HTML: https://mlsanity.com/paper/2403.17231/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 44 references that are in the map.
- First posted on arXiv 25 Mar 2024.

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

- [Differentiable Convex Optimization Layers](https://mlsanity.com/paper/1910.12430/) (2019)
- [GNM: A General Navigation Model to Drive Any Robot](https://mlsanity.com/paper/2210.03370/) (2022)
- [Socially Compliant Navigation Dataset (SCAND): A Large-Scale Dataset of Demonstrations for Social Navigation](https://mlsanity.com/paper/2203.15041/) (2022)
- [Virtual-to-real Deep Reinforcement Learning: Continuous Control of Mobile Robots for Mapless Navigation](https://mlsanity.com/paper/1703.00420/) (2017)
- [Motion Planning and Control for Mobile Robot Navigation Using Machine Learning: a Survey](https://mlsanity.com/paper/2011.13112/) (2020)
- [EGO-Planner: An ESDF-free Gradient-based Local Planner for Quadrotors](https://mlsanity.com/paper/2008.08835/) (2020)
- [From Perception to Decision: A Data-driven Approach to End-to-end Motion Planning for Autonomous Ground Robots](https://mlsanity.com/paper/1609.07910/) (2016)
- [Mobile Robot Path Planning in Dynamic Environments through Globally Guided Reinforcement Learning](https://mlsanity.com/paper/2005.05420/) (2020)
- [VOILA: Visual-Observation-Only Imitation Learning for Autonomous Navigation](https://mlsanity.com/paper/2105.09371/) (2021)
- [Toward Human-Like Social Robot Navigation: A Large-Scale, Multi-Modal, Social Human Navigation Dataset](https://mlsanity.com/paper/2303.14880/) (2023)
- [Learning Model Predictive Controllers with Real-Time Attention for Real-World Navigation](https://mlsanity.com/paper/2209.10780/) (2022)
- [Learning Inverse Kinodynamics for Accurate High-Speed Off-Road Navigation on Unstructured Terrain](https://mlsanity.com/paper/2102.12667/) (2021)
- [Multi-agent Motion Planning for Dense and Dynamic Environments via Deep Reinforcement Learning](https://mlsanity.com/paper/2001.06627/) (2020)
- [VI-IKD: High-Speed Accurate Off-Road Navigation using Learned Visual-Inertial Inverse Kinodynamics](https://mlsanity.com/paper/2203.15983/) (2022)
- [APPLD: Adaptive Planner Parameter Learning from Demonstration](https://mlsanity.com/paper/2004.00116/) (2020)
- [APPL: Adaptive Planner Parameter Learning](https://mlsanity.com/paper/2105.07620/) (2021)
- [APPLI: Adaptive Planner Parameter Learning From Interventions](https://mlsanity.com/paper/2011.00400/) (2020)
- [Benchmarking Reinforcement Learning Techniques for Autonomous Navigation](https://mlsanity.com/paper/2210.04839/) (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)
- [APPLE: Adaptive Planner Parameter Learning from Evaluative Feedback](https://mlsanity.com/paper/2108.09801/) (2021)
- [Rethinking Social Robot Navigation: Leveraging the Best of Two Worlds](https://mlsanity.com/paper/2309.13466/) (2023)
- [Conflict Avoidance in Social Navigation -- a Survey](https://mlsanity.com/paper/2106.12113/) (2021)
- [Toward Agile Maneuvers in Highly Constrained Spaces: Learning from Hallucination](https://mlsanity.com/paper/2007.14479/) (2020)
- [From Agile Ground to Aerial Navigation: Learning from Learned Hallucination](https://mlsanity.com/paper/2108.09793/) (2021)
- [Autonomous Navigation of AGVs in Unknown Cluttered Environments: log-MPPI Control Strategy](https://mlsanity.com/paper/2203.16599/) (2022)
- [Agile Robot Navigation through Hallucinated Learning and Sober Deployment](https://mlsanity.com/paper/2010.08098/) (2020)
- [Motion Memory: Leveraging Past Experiences to Accelerate Future Motion Planning](https://mlsanity.com/paper/2310.06198/) (2023)
- [Learning Perceptual Hallucination for Multi-Robot Navigation in Narrow Hallways](https://mlsanity.com/paper/2209.13641/) (2022)

## Built on by (most cited first, 1 of 1)

- [Reinforcement Learning for Wheeled Mobility on Vertically Challenging Terrain](https://mlsanity.com/paper/2409.02383/) (2024)

## Abstract (arXiv)

This paper introduces Dynamic Learning from Learned Hallucination (Dyna-LfLH), a self-supervised method for training motion planners to navigate environments with dense and dynamic obstacles. Classical planners struggle with dense, unpredictable obstacles due to limited computation, while learning-based planners face challenges in acquiring high-quality demonstrations for imitation learning or dealing with exploration inefficiencies in reinforcement learning. Building on Learning from Hallucination (LfH), which synthesizes training data from past successful navigation experiences in simpler environments, Dyna-LfLH incorporates dynamic obstacles by generating them through a learned latent distribution. This enables efficient and safe motion planner training. We evaluate Dyna-LfLH on a ground robot in both simulated and real environments, achieving up to a 25% improvement in success rate compared to baselines.