# Learning Panorama-Aware VLA for Mobile Manipulation with Whole-Body Teleoperation arXiv: https://arxiv.org/abs/2608.02257 · HTML: https://mlsanity.com/paper/2608.02257/ ## Key facts - Cited by 4 papers in the map (data through 14 Oct 2026). - Placed in Human-video and latent-action pretraining, one of 13 directions in Vision-language-action models. - Builds on 35 of its 37 references that are in the map. - First posted on arXiv 3 Aug 2026. ## Placed in - Vision-language-action models / [Human-video and latent-action pretraining](https://mlsanity.com/vla/human-video-and-latent-action-pretraining/) ## Builds on (most cited first, 30 of 35) - [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) - [Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware](https://mlsanity.com/paper/2304.13705/) (2023) - [Octo: An Open-Source Generalist Robot Policy](https://mlsanity.com/paper/2405.12213/) (2024) - [Open X-Embodiment: Robotic Learning Datasets and RT-X Models](https://mlsanity.com/paper/2310.08864/) (2023) - [RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation](https://mlsanity.com/paper/2410.07864/) (2024) - [Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots](https://mlsanity.com/paper/2402.10329/) (2024) - [GELLO: A General, Low-Cost, and Intuitive Teleoperation Framework for Robot Manipulators](https://mlsanity.com/paper/2309.13037/) (2023) - [OmniH2O: Universal and Dexterous Human-to-Humanoid Whole-Body Teleoperation and Learning](https://mlsanity.com/paper/2406.08858/) (2024) - [DexCap: Scalable and Portable Mocap Data Collection System for Dexterous Manipulation](https://mlsanity.com/paper/2403.07788/) (2024) - [Open-TeleVision: Teleoperation with Immersive Active Visual Feedback](https://mlsanity.com/paper/2407.01512/) (2024) - [π0.7: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities](https://mlsanity.com/paper/2604.15483/) (2026) - [Learning Human-to-Humanoid Real-Time Whole-Body Teleoperation](https://mlsanity.com/paper/2403.04436/) (2024) - [AHA: A Vision-Language-Model for Detecting and Reasoning Over Failures in Robotic Manipulation](https://mlsanity.com/paper/2410.00371/) (2024) - [TWIST: Teleoperated Whole-Body Imitation System](https://mlsanity.com/paper/2505.02833/) (2025) - [Bunny-VisionPro: Real-Time Bimanual Dexterous Teleoperation for Imitation Learning](https://mlsanity.com/paper/2407.03162/) (2024) - [Retargeting Matters: General Motion Retargeting for Humanoid Motion Tracking](https://mlsanity.com/paper/2510.02252/) (2025) - [OPEN TEACH: A Versatile Teleoperation System for Robotic Manipulation](https://mlsanity.com/paper/2403.07870/) (2024) - [DepthVLA: Enhancing Vision-Language-Action Models with Depth-Aware Spatial Reasoning](https://mlsanity.com/paper/2510.13375/) (2025) - [FastUMI: A Scalable and Hardware-Independent Universal Manipulation Interface with Dataset](https://mlsanity.com/paper/2409.19499/) (2024) - [MoManipVLA: Transferring Vision-language-action Models for General Mobile Manipulation](https://mlsanity.com/paper/2503.13446/) (2025) - [BEHAVIOR Robot Suite: Streamlining Real-World Whole-Body Manipulation for Everyday Household Activities](https://mlsanity.com/paper/2503.05652/) (2025) - [TeleMoMa: A Modular and Versatile Teleoperation System for Mobile Manipulation](https://mlsanity.com/paper/2403.07869/) (2024) - [SPIN: Simultaneous Perception, Interaction and Navigation](https://mlsanity.com/paper/2405.07991/) (2024) - [From Foundation to Application: Improving VLA Models in Practice](https://mlsanity.com/paper/2607.06403/) (2026) - [AC-DiT: Adaptive Coordination Diffusion Transformer for Mobile Manipulation](https://mlsanity.com/paper/2507.01961/) (2025) - [Harmonic Mobile Manipulation](https://mlsanity.com/paper/2312.06639/) (2023) - [HoMMI: Learning Whole-Body Mobile Manipulation from Human Demonstrations](https://mlsanity.com/paper/2603.03243/) (2026) - [RoboPanoptes: The All-seeing Robot with Whole-body Dexterity](https://mlsanity.com/paper/2501.05420/) (2025) ## Built on by (most cited first, 4 of 4) - [MoPA: Coordinated Mobile Manipulation via Subsystem-Specific Perception Alignment](https://mlsanity.com/paper/2609.12081/) (2026) - [PanoFuse: Panorama-Enhanced Vision-Language-Action Learning with Decoupled Semantic-Geometric Routing](https://mlsanity.com/paper/2609.31717/) (2026) - [UniWAM Technical Report: Unified Mobile Manipulation via Mixed-Stream World-Action Modeling and Manipulation Anchor Pose Supervision](https://mlsanity.com/paper/2609.39388/) (2026) - [UniTrackPLA: Unified Panorama-Language-Action Model for Instruction-Guided Navigation and Dynamic Person Tracking](https://mlsanity.com/paper/2610.00878/) (2026) ## Abstract (arXiv) Mobile manipulation is a key capability for embodied intelligence, enabling robots to accomplish complex multi-stage tasks in open-world environments. However, mobile manipulation poses two key challenges for vision-language-action (VLA) policies: At the data level, the efficient collection of high-quality whole-body demonstrations demands the coordinated control of both the mobile base and the robotic arms; at the model level, existing VLA models predominantly rely on local camera observations, whose limited field of view hinders global spatial understanding. To address these challenges, we develop a whole-body teleoperation system and a panoramic-aware VLA policy. The system enables coordinated control of a wheeled bimanual robot through a single VR interface and supports the acquisition of a real-world mobile manipulation dataset comprising 5.5 hours of multimodal demonstrations. Building upon this dataset, we propose PanoVLA, a panorama-aware vision-language-action policy for mobile bimanual manipulation. Built upon a Mixture-of-Transformers architecture, PanoVLA introduces global spatial context through dedicated panorama encoding and fusion modules, enabling effective integration of panoramic observations with language instructions and robot states for action generation. Evaluation on four real-world mobile manipulation tasks demonstrates that PanoVLA achieves an average stage completion rate of 91.3% and an end-to-end success rate of 73.4%, substantially outperforming local-view baselines. These results demonstrate that incorporating panoramic spatial context improves spatial understanding and closed-loop manipulation performance in mobile robots.