AnywhereVLA：语言条件探索与移动操作
AnywhereVLA: Language-Conditioned Exploration and Mobile Manipulation
AI总结：
AnywhereVLA提出模块化移动操作框架，结合经典SLAM与微调VLA，实现未知室内环境中的语言引导拾取放置，达到46%任务成功率。
摘要：
我们通过AnywhereVLA解决了在未知、不可预测的室内环境中进行自然语言拾取和放置的问题，这是一个用于移动操作的模块化框架。用户文本提示作为入口点，被解析为结构化任务图，该图条件化使用LiDAR和相机的经典SLAM、度量语义映射以及任务感知的前沿探索策略。然后，一个接近规划器选择具有可见性和可达性感知的预抓取基座姿态。对于交互，一个紧凑的SmolVLA操作头在TheRobotStudio的SO-101平台上针对平台拾取和放置轨迹进行微调，将局部视觉上下文和子目标落地为抓取和放置提议。整个系统完全在消费级硬件上机载运行，使用Jetson Orin NX进行感知和VLA，使用Intel NUC进行SLAM、探索和控制，维持实时操作。我们在多房间实验室中，在静态场景和正常人体运动下评估了AnywhereVLA。在此设置中，系统实现了46%的总体任务成功率，同时在嵌入式计算上保持吞吐量。通过将经典堆栈与微调的VLA操作相结合，系统继承了基于几何导航的可靠性以及语言条件操作的敏捷性和任务泛化能力。
英文摘要：
We address natural language pick-and-place in unseen, unpredictable indoor environments with AnywhereVLA, a modular framework for mobile manipulation. A user text prompt serves as an entry point and is parsed into a structured task graph that conditions classical SLAM with LiDAR and cameras, metric semantic mapping, and a task-aware frontier exploration policy. An approach planner then selects visibility and reachability aware pre grasp base poses. For interaction, a compact SmolVLA manipulation head is fine tuned on platform pick and place trajectories for the SO-101 by TheRobotStudio, grounding local visual context and sub-goals into grasp and place proposals. The full system runs fully onboard on consumer-level hardware, with Jetson Orin NX for perception and VLA and an Intel NUC for SLAM, exploration, and control, sustaining real-time operation. We evaluated AnywhereVLA in a multi-room lab under static scenes and normal human motion. In this setting, the system achieves a $46\%$ overall task success rate while maintaining throughput on embedded compute. By combining a classical stack with a fine-tuned VLA manipulation, the system inherits the reliability of geometry-based navigation with the agility and task generalization of language-conditioned manipulation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
