# LaND: Learning to Navigate from Disengagements

arXiv: https://arxiv.org/abs/2010.04689 · HTML: https://mlsanity.com/paper/2010.04689/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2020.

## Built on by (most cited first, 25 of 25)

- [End-to-End Urban Driving by Imitating a Reinforcement Learning Coach](https://mlsanity.com/paper/2108.08265/) (2021)
- [LazyDAgger: Reducing Context Switching in Interactive Imitation Learning](https://mlsanity.com/paper/2104.00053/) (2021)
- [Learning Robust Autonomous Navigation and Locomotion for Wheeled-Legged Robots](https://mlsanity.com/paper/2405.01792/) (2024)
- [CityWalker: Learning Embodied Urban Navigation from Web-Scale Videos](https://mlsanity.com/paper/2411.17820/) (2024)
- [Fleet-DAgger: Interactive Robot Fleet Learning with Scalable Human Supervision](https://mlsanity.com/paper/2206.14349/) (2022)
- [RLIF: Interactive Imitation Learning as Reinforcement Learning](https://mlsanity.com/paper/2311.12996/) (2023)
- [FastRLAP: A System for Learning High-Speed Driving via Deep RL and Autonomous Practicing](https://mlsanity.com/paper/2304.09831/) (2023)
- [Proactive Anomaly Detection for Robot Navigation with Multi-Sensor Fusion](https://mlsanity.com/paper/2204.01146/) (2022)
- [Learning Robotic Navigation from Experience: Principles, Methods, and Recent Results](https://mlsanity.com/paper/2212.06759/) (2022)
- [Reinforcement Learning for Collision-free Flight Exploiting Deep Collision Encoding](https://mlsanity.com/paper/2402.03947/) (2024)
- [DTG : Diffusion-based Trajectory Generation for Mapless Global Navigation](https://mlsanity.com/paper/2403.09900/) (2024)
- [STERLING: Self-Supervised Terrain Representation Learning from Unconstrained Robot Experience](https://mlsanity.com/paper/2309.15302/) (2023)
- [Metis: A Generalizable and Efficient World-Action Model for Autonomous Driving and Urban Navigation](https://mlsanity.com/paper/2606.15869/) (2026)
- [Goal-Guided Transformer-Enabled Reinforcement Learning for Efficient Autonomous Navigation](https://mlsanity.com/paper/2301.00362/) (2023)
- [CREStE: Scalable Mapless Navigation with Internet Scale Priors and Counterfactual Guidance](https://mlsanity.com/paper/2503.03921/) (2025)
- [PACER: Preference-conditioned All-terrain Costmap Generation](https://mlsanity.com/paper/2410.23488/) (2024)
- [Your Vision-Language-Action Model Already Has Attention Heads For Path Deviation Detection](https://mlsanity.com/paper/2603.13782/) (2026)
- [Interactive Imitation Learning for Dexterous Robotic Manipulation: Challenges and Perspectives -- A Survey](https://mlsanity.com/paper/2506.00098/) (2025)
- [SAFER: Safe Collision Avoidance using Focused and Efficient Trajectory Search with Reinforcement Learning](https://mlsanity.com/paper/2209.11789/) (2022)
- [Guided Cooperation in Hierarchical Reinforcement Learning via Model-based Rollout](https://mlsanity.com/paper/2309.13508/) (2023)
- [Circular Accessible Depth: A Robust Traversability Representation for UGV Navigation](https://mlsanity.com/paper/2212.13676/) (2022)
- [A GP-based Robust Motion Planning Framework for Agile Autonomous Robot Navigation and Recovery in Unknown Environments](https://mlsanity.com/paper/2402.01617/) (2024)
- [An Expert Ensemble for Detecting Anomalous Scenes, Interactions, and Behaviors in Autonomous Driving](https://mlsanity.com/paper/2502.16389/) (2025)
- [Residual Reward Models: Leveraging Prior Knowledge for Efficient Preference-based Reinforcement Learning in Robotics](https://mlsanity.com/paper/2507.00611/) (2025)
- [ASkDAgger: Active Skill-level Data Aggregation for Interactive Imitation Learning](https://mlsanity.com/paper/2508.05310/) (2025)

## Abstract (arXiv)

Consistently testing autonomous mobile robots in real world scenarios is a necessary aspect of developing autonomous navigation systems. Each time the human safety monitor disengages the robot's autonomy system due to the robot performing an undesirable maneuver, the autonomy developers gain insight into how to improve the autonomy system. However, we believe that these disengagements not only show where the system fails, which is useful for troubleshooting, but also provide a direct learning signal by which the robot can learn to navigate. We present a reinforcement learning approach for learning to navigate from disengagements, or LaND. LaND learns a neural network model that predicts which actions lead to disengagements given the current sensory observation, and then at test time plans and executes actions that avoid disengagements. Our results demonstrate LaND can successfully learn to navigate in diverse, real world sidewalk environments, outperforming both imitation learning and reinforcement learning approaches. Videos, code, and other material are available on our website https://sites.google.com/view/sidewalk-learning