NeuralParker: A Reinforcement Learning Planner for Irregular Parking Environments
cs.RO, cs.LG
Submitted: 2026-08-25
Updated: 2026-08-25
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Adapting Reinforcement Learning for Path Planning in Constrained Parking Scenarios
- MultiPark: Multimodal Parking Transformer with Next-Segment Prediction
- N3P: Accelerated Automated Parking via a Learning-Based Naturalistic Three-Stage Scheme
- A Diffusion-Refined Planner with Reinforcement Learning Priors for Confined-Space Parking
- TransParking: A Dual-Decoder Transformer Framework with Soft Localization for End-to-End Automatic Parking
- Proximal Policy Optimization Algorithms
Related papers
- FMT x: An Efficient and Asymptotically Optimal Extension of the Fast Marching Tree for Dynamic Replanning
- MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
- RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
- HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
- APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies
- Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving