# APPL: Adaptive Planner Parameter Learning

arXiv: https://arxiv.org/abs/2105.07620 · HTML: https://mlsanity.com/paper/2105.07620/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 May 2021.

## Built on by (most cited first, 20 of 20)

- [Socially Compliant Navigation Dataset (SCAND): A Large-Scale Dataset of Demonstrations for Social Navigation](https://mlsanity.com/paper/2203.15041/) (2022)
- [Learning Model Predictive Controllers with Real-Time Attention for Real-World Navigation](https://mlsanity.com/paper/2209.10780/) (2022)
- [Benchmarking Reinforcement Learning Techniques for Autonomous Navigation](https://mlsanity.com/paper/2210.04839/) (2022)
- [High-Speed Accurate Robot Control using Learned Forward Kinodynamics and Non-linear Least Squares Optimization](https://mlsanity.com/paper/2206.08487/) (2022)
- [Reinforcement Learning for Wheeled Mobility on Vertically Challenging Terrain](https://mlsanity.com/paper/2409.02383/) (2024)
- [A Reinforcement Learning-Boosted Motion Planning Framework: Comprehensive Generalization Performance in Autonomous Driving](https://mlsanity.com/paper/2402.01465/) (2024)
- [VANP: Learning Where to See for Navigation with Self-Supervised Vision-Action Pre-Training](https://mlsanity.com/paper/2403.08109/) (2024)
- [Grounded Curriculum Learning](https://mlsanity.com/paper/2409.19816/) (2024)
- [PIC4rl-gym: a ROS2 modular framework for Robots Autonomous Navigation with Deep Reinforcement Learning](https://mlsanity.com/paper/2211.10714/) (2022)
- [CaRE: Finding Root Causes of Configuration Issues in Highly-Configurable Robots](https://mlsanity.com/paper/2301.07690/) (2023)
- [A Study on Learning Social Robot Navigation with Multimodal Perception](https://mlsanity.com/paper/2309.12568/) (2023)
- [VertiSelector: Automatic Curriculum Learning for Wheeled Mobility on Vertically Challenging Terrain](https://mlsanity.com/paper/2409.17469/) (2024)
- [GACL: Grounded Adaptive Curriculum Learning with Active Task and Performance Monitoring](https://mlsanity.com/paper/2508.02988/) (2025)
- [Robot Navigation with Reinforcement Learned Path Generation and Fine-Tuned Motion Control](https://mlsanity.com/paper/2210.10639/) (2022)
- [Dyna-LfLH: Learning Agile Navigation in Dynamic Environments from Learned Hallucination](https://mlsanity.com/paper/2403.17231/) (2024)
- [Adaptive Social Force Window Planner with Reinforcement Learning](https://mlsanity.com/paper/2404.13678/) (2024)
- [Reinforcement Learning for Adaptive Planner Parameter Tuning: A Perspective on Hierarchical Architecture](https://mlsanity.com/paper/2503.18366/) (2025)
- [NavG: Risk-Aware Navigation in Crowded Environments Based on Reinforcement Learning with Guidance Points](https://mlsanity.com/paper/2503.02111/) (2025)
- [APPLV: Adaptive Planner Parameter Learning from Vision-Language-Action Model](https://mlsanity.com/paper/2603.08862/) (2026)
- [HumAIN: Human-Aware Implicit Social Robot Navigation](https://mlsanity.com/paper/2607.07357/) (2026)

## Abstract (arXiv)

While current autonomous navigation systems allow robots to successfully drive themselves from one point to another in specific environments, they typically require extensive manual parameter re-tuning by human robotics experts in order to function in new environments. Furthermore, even for just one complex environment, a single set of fine-tuned parameters may not work well in different regions of that environment. These problems prohibit reliable mobile robot deployment by non-expert users. As a remedy, we propose Adaptive Planner Parameter Learning (APPL), a machine learning framework that can leverage non-expert human interaction via several modalities -- including teleoperated demonstrations, corrective interventions, and evaluative feedback -- and also unsupervised reinforcement learning to learn a parameter policy that can dynamically adjust the parameters of classical navigation systems in response to changes in the environment. APPL inherits safety and explainability from classical navigation systems while also enjoying the benefits of machine learning, i.e., the ability to adapt and improve from experience. We present a suite of individual APPL methods and also a unifying cycle-of-learning scheme that combines all the proposed methods in a framework that can improve navigation performance through continual, iterative human interaction and simulation training.