mlsanityData to 14 Oct 2026

APPLD: Adaptive Planner Parameter Learning from Demonstration

arXiv 2004.00116 ↗First posted 31 Mar 2020cs.RO cs.LGcited by 20

On the map: Offline RL and imitation learning from data → Language-conditioned policies and VLAs

Abstract (arXiv)

Existing autonomous robot navigation systems allow robots to move from one point to another in a collision-free manner. However, when facing new environments, these systems generally require re-tuning by expert roboticists with a good understanding of the inner workings of the navigation system. In contrast, even users who are unversed in the details of robot navigation algorithms can generate desirable navigation behavior in new environments via teleoperation. In this paper, we introduce APPLD, Adaptive Planner Parameter Learning from Demonstration, that allows existing navigation systems to be successfully applied to new complex environments, given only a human teleoperated demonstration of desirable navigation. APPLD is verified on two robots running different navigation systems in different environments. Experimental results show that APPLD can outperform navigation systems with the default and expert-tuned parameters, and even the human demonstrator themselves.

Key facts. Cited by 20 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 5 of its 24 references that are in the map. First posted on arXiv 31 Mar 2020.

Builds on

5 of 24 references.

References · 5

  1. 2020BADGR: An Autonomous Self-Supervised Learning-Based Navigation Systemcited by 102
  2. 2016From Perception to Decision: A Data-driven Approach to End-to-end Motion Planning for Autonomous Ground Robotscited by 44
  3. 2019Differentiable Gaussian Process Motion Planningcited by 28
  4. 2018Learning Human-Aware Path Planning with Fully Convolutional Networkscited by 11
  5. 2017ROS Navigation Tuning Guidecited by 9

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 3; 2022: 4; 2023: 4; 2024: 6; 2025: 2; 2026: 1.

20 papers.

Sort:

By area:

  1. 2024VLM-Social-Nav: Socially Aware Robot Navigation through Scoring using Vision-Language Modelscited by 46
  2. 2022Learning Model Predictive Controllers with Real-Time Attention for Real-World Navigationcited by 29
  3. 2021Self-Imitation Learning by Planningcited by 26
  4. 2021A Survey on Human-aware Robot Navigationcited by 24
  5. 2022Benchmarking Reinforcement Learning Techniques for Autonomous Navigationcited by 19
  6. 2022High-Speed Accurate Robot Control using Learned Forward Kinodynamics and Non-linear Least Squares Optimizationcited by 11
  7. 2024Reinforcement Learning for Wheeled Mobility on Vertically Challenging Terraincited by 7
  8. 2021AutoTune: Controller Tuning for High-Speed Flightcited by 5
  9. 2024Grounded Curriculum Learningcited by 5
  10. 2023Reinforcement Learning in Robotic Motion Planning by Combined Experience-based Planning and Self-Imitation Learningcited by 4
  11. 2023A Study on Learning Social Robot Navigation with Multimodal Perceptioncited by 4
  12. 2022Learning Personalized Human-Aware Robot Navigation Using Virtual Reality Demonstrations from a User Studycited by 3
8 more
  1. 2023Arena-Rosnav 2.0: A Development and Benchmarking Platform for Robot Navigation in Highly Dynamic Environmentscited by 3
  2. 2024VertiSelector: Automatic Curriculum Learning for Wheeled Mobility on Vertically Challenging Terraincited by 3
  3. 2025GACL: Grounded Adaptive Curriculum Learning with Active Task and Performance Monitoringcited by 3
  4. 2023Arena-Web -- A Web-based Development and Benchmarking Platform for Autonomous Navigation Approachescited by 1
  5. 2024Dyna-LfLH: Learning Agile Navigation in Dynamic Environments from Learned Hallucinationcited by 1
  6. 2025Reinforcement Learning for Adaptive Planner Parameter Tuning: A Perspective on Hierarchical Architecturecited by 1
  7. 2024Aligning Robot Navigation Behaviors with Human Intentions and Preferencescited by 0
  8. 2026APPLV: Adaptive Planner Parameter Learning from Vision-Language-Action Modelcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Adaptive Dynamics Planning for Robot Navigation
  2. 2024Hybrid Classical/RL Local Planner for Ground Robot Navigation
  3. 2024Demonstration-Enhanced Adaptable Multi-Objective Robot Navigation
  4. 2025Enhanced Robotic Navigation in Deformable Environments using Learning from Demonstration and Dynamic Modulation
  5. 2025Self-Augmented Robot Trajectory: Efficient Imitation Learning via Safe Self-augmentation with Demonstrator-annotated Precision
  6. 2025ComposableNav: Instruction-Following Navigation in Dynamic Environments via Composable Diffusion
  7. 2020Learning Navigation Costs from Demonstration in Partially Observable Environments
  8. 2023Learning whom to trust in navigation: dynamically switching between classical and neural planning