AI RESEARCH
APPLV: Adaptive Planner Parameter Learning from Vision-Language-Action Model
arXiv CS.LG
•
ArXi:2603.08862v1 Announce Type: cross 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.