{"ID":3050113,"CreatedAt":"2026-06-04T02:13:16.786527022Z","UpdatedAt":"2026-06-06T10:38:57.550619125Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.04708","arxiv_id":"2606.04708","title":"VISTA: Vision-Grounded and Physics-Validated Adaptation of UMI data for VLA Training","abstract":"Universal Manipulation Interface (UMI) enables scalable real-world robot data collection without hardware-specific teleoperation, yet leveraging UMI data to train large-scale Vision-Language-Action (VLA) models remains fundamentally challenging. We identify two critical mismatches: wrist-mounted fisheye views, with severe radial distortion and local gripper-centric perspectives, are out-of-distribution for pretrained VLMs; and human-collected trajectories frequently violate kinematic limits, incur collisions, or exceed controller bandwidth, teaching VLA policies physically infeasible actions. To address the challenges, we present VISTA, a framework that bridges this dual gap through three synergistic components. (i)~UMI-VQA, the first large-scale VQA dataset tailored to wrist-mounted fisheye observations, aligns VLM representations to the distorted visual regime via auxiliary vision-language supervision. (ii)~A systematic physical-validation pipeline performs a data-completeness pre-check and scores each valid trajectory for trajectory continuity, self-collision risk, and execution fidelity before it enters training. (iii)~A two-stage co-training recipe jointly learns vision-language grounding on UMI-VQA and action prediction on validated trajectories. Our experiments empirically show that incorporating UMI-VQA consistently improves downstream policy performance, and that physical-validation scores are strongly predictive of deployment success. On diverse simulation and real-world manipulation tasks, VISTA significantly outperforms strong baselines including $π_{0.5}$, LingBot-VLA, and Wall-X. We release the physical-validation pipeline, UMI-VQA, validated trajectory data, and the pre-trained model for the community.","short_abstract":"Universal Manipulation Interface (UMI) enables scalable real-world robot data collection without hardware-specific teleoperation, yet leveraging UMI data to train large-scale Vision-Language-Action (VLA) models remains fundamentally challenging. We identify two critical mismatches: wrist-mounted fisheye views, with sev...","url_abs":"https://arxiv.org/abs/2606.04708","url_pdf":"https://arxiv.org/pdf/2606.04708v1","authors":"[\"Siyuan Yang\",\"Linzheng Guo\",\"Ouyang Lu\",\"Zhaxizhuoma\",\"Daoran Zhang\",\"Xinmiao Wang\",\"Ting Xiao\",\"Fangzheng Yan\",\"Zhijun Chen\",\"Yan Ding\",\"Chao Yu\",\"Chenjia Bai\",\"Xuelong Li\"]","published":"2026-06-03T10:38:45Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.AI\"]","methods":"[]","has_code":false}