WayFinder：用于零样本航点生成和低级运动控制的分层视觉-语言-动作框架
WayFinder: Hierarchical Visual-Language-Action for Zero-Shot Waypoint Generation and Low-Level Kinematic Control
- San Diego State University（圣地亚哥州立大学）
- University of California, Irvine（加利福尼亚大学尔湾分校）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
WayFinder提出分层VLA框架，解耦高级推理与低级控制，零样本生成航点，在AirSim中提升导航成功率最高27.45%，无需微调。
AI中文摘要：
视觉-语言-动作（VLA）模型为自主机器人提供了前所未有的泛化能力；然而，其实际部署常常因执行不可靠以及针对特定机器人形态和任务进行微调的计算成本过高而受阻。为弥合这一差距，我们提出了WayFinder，一种端到端、闭环的分层VLA框架，通过将高级任务推理与低级运动控制解耦，规避了微调的需求。WayFinder利用零样本、车外多模态大语言模型（MLLM）策略处理语言上下文和状态图，以进行策略性航点生成。异步地，一个轻量级的车载策略基于连续传感器反馈，以高频执行实时运动控制。我们在微软AirSim中评估了WayFinder，测试了四种不同复杂度的环境和三种MLLM规模，以平衡预测效能与计算效率。结果表明，与基线低级策略相比，WayFinder实现了更优越的导航可靠性。通过在导航失败时仅查询高级MLLM，WayFinder消除了微调需求，最小化了昂贵的推理，并显著提高了导航成功率，最高提升达27.45%。
英文摘要：
Visual Language Action (VLA) models offer unprecedented generalization for autonomous robots; however, their real-world deployment is frequently bottlenecked by unreliable execution and the prohibitive computational cost of fine-tuning for specific robot embodiments and tasks. To bridge this gap, we propose WayFinder, an end-to-end, closed-loop hierarchical VLA framework that circumvents the need for fine-tuning by decoupling high-level task reasoning from low-level kinematic control. WayFinder utilizes a zero-shot, offboard Multimodal Large Language Model (MLLM) policy to process linguistic context and state maps for strategic waypoint generation. Asynchronously, a lightweight, onboard policy executes real-time kinematic control at high frequency based on continuous sensor feedback. We evaluate WayFinder in Microsoft AirSim, testing on four environments of varying complexity and three MLLM scales to balance prediction efficacy with computational efficiency. Our results demonstrate that WayFinder achieves superior navigation reliability compared to baseline low-level policies. By querying the high-level MLLM only during navigation failures, WayFinder eliminates the need for fine-tuning, minimizes expensive inferences, and significantly increases navigation success rates by up to 27.45%.
