# AnywhereVLA: Language-Conditioned Exploration and Mobile Manipulation

arXiv: https://arxiv.org/abs/2509.21006 · HTML: https://mlsanity.com/paper/2509.21006/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 29 references that are in the map.
- First posted on arXiv 25 Sep 2025.

## Builds on (most cited first, 22 of 22)

- [OpenVLA: An Open-Source Vision-Language-Action Model](https://mlsanity.com/paper/2406.09246/) (2024)
- [π0: A Vision-Language-Action Flow Model for General Robot Control](https://mlsanity.com/paper/2410.24164/) (2024)
- [π0.5: a Vision-Language-Action Model with Open-World Generalization](https://mlsanity.com/paper/2504.16054/) (2025)
- [RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation](https://mlsanity.com/paper/2410.07864/) (2024)
- [SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics](https://mlsanity.com/paper/2506.01844/) (2025)
- [Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation](https://mlsanity.com/paper/2401.02117/) (2024)
- [TinyVLA: Towards Fast, Data-Efficient Vision-Language-Action Models for Robotic Manipulation](https://mlsanity.com/paper/2409.12514/) (2024)
- [A Closer Look at Deep Learning Heuristics: Learning rate restarts, Warmup and Distillation](https://mlsanity.com/paper/1810.13243/) (2018)
- [The Marathon 2: A Navigation System](https://mlsanity.com/paper/2003.00368/) (2020)
- [Large VLM-based Vision-Language-Action Models for Robotic Manipulation: A Survey](https://mlsanity.com/paper/2508.13073/) (2025)
- [FAST-LIVO2: Fast, Direct LiDAR-Inertial-Visual Odometry](https://mlsanity.com/paper/2408.14035/) (2024)
- [ASC: Adaptive Skill Coordination for Robotic Mobile Manipulation](https://mlsanity.com/paper/2304.00410/) (2023)
- [MoManipVLA: Transferring Vision-language-action Models for General Mobile Manipulation](https://mlsanity.com/paper/2503.13446/) (2025)
- [BUMBLE: Unifying Reasoning and Acting with Vision-Language Models for Building-wide Mobile Manipulation](https://mlsanity.com/paper/2410.06237/) (2024)
- [AC-DiT: Adaptive Coordination Diffusion Transformer for Mobile Manipulation](https://mlsanity.com/paper/2507.01961/) (2025)
- [TidyBot++: An Open-Source Holonomic Mobile Manipulator for Robot Learning](https://mlsanity.com/paper/2412.10447/) (2024)
- [EdgeVLA: Efficient Vision-Language-Action Models](https://mlsanity.com/paper/2507.14049/) (2025)
- [Demonstrating Mobile Manipulation in the Wild: A Metrics-Driven Approach](https://mlsanity.com/paper/2401.01474/) (2024)
- [Natural Language Instructions for Intuitive Human Interaction with Robotic Assistants in Field Construction Work](https://mlsanity.com/paper/2307.04195/) (2023)
- [TacMMs: Tactile Mobile Manipulators for Warehouse Automation](https://mlsanity.com/paper/2306.16783/) (2023)
- [Practical Insights on Grasp Strategies for Mobile Manipulation in the Wild](https://mlsanity.com/paper/2504.12512/) (2025)
- [Language-Guided Long Horizon Manipulation with LLM-based Planning and Visual Perception](https://mlsanity.com/paper/2509.02324/) (2025)

## Built on by (most cited first, 3 of 3)

- [EchoVLA: Robotic Vision-Language-Action Model with Synergistic Declarative Memory for Mobile Manipulation](https://mlsanity.com/paper/2511.18112/) (2025)
- [Exploration and Exploitation Errors Are Measurable for Language Model Agents](https://mlsanity.com/paper/2604.13151/) (2026)
- [LiteVLA-H: Dual-Rate Vision-Language-Action Inference for Onboard Aerial Guidance and Semantic Perception](https://mlsanity.com/paper/2605.00884/) (2026)

## Abstract (arXiv)

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.