AnywhereVLA: Language-Conditioned Exploration and Mobile Manipulation
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| author | Gubernatorov, Konstantin Voronov, Artem Voronov, Roman Pasynkov, Sergei Perminov, Stepan Guo, Ziang Tsetserukou, Dzmitry |
| author_facet | Gubernatorov, Konstantin Voronov, Artem Voronov, Roman Pasynkov, Sergei Perminov, Stepan Guo, Ziang Tsetserukou, Dzmitry |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_21006 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AnywhereVLA: Language-Conditioned Exploration and Mobile Manipulation Gubernatorov, Konstantin Voronov, Artem Voronov, Roman Pasynkov, Sergei Perminov, Stepan Guo, Ziang Tsetserukou, Dzmitry Robotics Artificial Intelligence 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. |
| title | AnywhereVLA: Language-Conditioned Exploration and Mobile Manipulation |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2509.21006 |