# APPLE: Adaptive Planner Parameter Learning from Evaluative Feedback

arXiv: https://arxiv.org/abs/2108.09801 · HTML: https://mlsanity.com/paper/2108.09801/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Aug 2021.

## Built on by (most cited first, 16 of 16)

- [Learning Model Predictive Controllers with Real-Time Attention for Real-World Navigation](https://mlsanity.com/paper/2209.10780/) (2022)
- [Risk-Aware Off-Road Navigation via a Learned Speed Distribution Map](https://mlsanity.com/paper/2203.13429/) (2022)
- [Benchmarking Reinforcement Learning Techniques for Autonomous Navigation](https://mlsanity.com/paper/2210.04839/) (2022)
- [Learning from Active Human Involvement through Proxy Value Propagation](https://mlsanity.com/paper/2502.03369/) (2025)
- [Primitive Skill-based Robot Learning from Human Evaluative Feedback](https://mlsanity.com/paper/2307.15801/) (2023)
- [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)
- [Grounded Curriculum Learning](https://mlsanity.com/paper/2409.19816/) (2024)
- [A Study on Learning Social Robot Navigation with Multimodal Perception](https://mlsanity.com/paper/2309.12568/) (2023)
- [RE-MOVE: An Adaptive Policy Design for Robotic Navigation Tasks in Dynamic Environments via Language-Based Feedback](https://mlsanity.com/paper/2303.07622/) (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)
- [Reinforcement Learning for Adaptive Planner Parameter Tuning: A Perspective on Hierarchical Architecture](https://mlsanity.com/paper/2503.18366/) (2025)
- [APPLV: Adaptive Planner Parameter Learning from Vision-Language-Action Model](https://mlsanity.com/paper/2603.08862/) (2026)

## Abstract (arXiv)

Classical autonomous navigation systems can control robots in a collision-free manner, oftentimes with verifiable safety and explainability. When facing new environments, however, fine-tuning of the system parameters by an expert is typically required before the system can navigate as expected. To alleviate this requirement, the recently-proposed Adaptive Planner Parameter Learning paradigm allows robots to learn how to dynamically adjust planner parameters using a teleoperated demonstration or corrective interventions from non-expert users. However, these interaction modalities require users to take full control of the moving robot, which requires the users to be familiar with robot teleoperation. As an alternative, we introduce textscapple, Adaptive Planner Parameter Learning from Evaluative Feedback (real-time, scalar-valued assessments of behavior), which represents a less-demanding modality of interaction. Simulated and physical experiments show textscapple can achieve better performance compared to the planner with static default parameters and even yield improvement over learned parameters from richer interaction modalities.