AnywhereVLA: 言語条件付き探索と移動マニピュレーション
AnywhereVLA: Language-Conditioned Exploration and Mobile Manipulation
未知の屋内環境で自然言語によるピックアンドプレースを実現するモジュール型移動マニピュレーションフレームワークを提案し、SLAMとVLAを組み合わせてエッジハードウェア上で46%の成功率を達成した。
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著者: Konstantin Gubernatorov, Artem Voronov, Roman Voronov, Sergei Pasynkov, Stepan Perminov, Ziang Guo, Dzmitry Tsetserukou
分類: cs.RO, cs.AI
原文アブストラクト
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.
