NeuralParker: A Reinforcement Learning Planner for Irregular Parking Environments
Automated parking commonly assumes marked slots and short approach maneuvers. Delivery and service vehicles, however, may need to reach an operator-specified pose in an irregular bounded environment from a distant start. Existing learning-based parking planners often rely on local observations, which can restrict long-range route reasoning. To address this problem, we present NeuralParker, a reinforcement learning-based hybrid planner for arbitrary-pose parking. NeuralParker encodes full-environment obstacle and boundary geometry in a target-relative vertex representation, allowing the policy to retain route-defining context throughout the approach. It further couples a learned curvature-...
| Authors | Zihan Wang, Bai Huang, Yang Guan et al. |
|---|---|
| Primary category | cs.RO |
| Published | 25 August 2026 |
| Last updated at source | 25 August 2026 |
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