APPLV: Adaptive Planner Parameter Learning from Vision-Language-Action Model
Autonomous navigation in highly constrained environments remains challenging for mobile robots. Classical navigation approaches offer safety assurances but require environment-specific parameter tuning; end-to-end learning bypasses parameter tuning but struggles with precise control in constrained spaces. To this end, recent robot learning approaches automate parameter tuning while retaining classical systems' safety, yet still face challenges in generalizing to unseen environments. Recently, Vision-Language-Action (VLA) models have shown promise by leveraging foundation models' scene understanding capabilities, but still struggle with precise control and inference latency in navigation t...
| Authors | Yuanjie Lu, Beichen Wang, Zhengqi Wu et al. |
|---|---|
| Primary category | cs.RO |
| Published | 9 March 2026 |
| Last updated at source | 14 July 2026 |
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