VISTA: Vision-Grounded and Physics-Validated Adaptation of UMI data for VLA Training

• URL: http://arxiv.org/abs/2606.04708v2
• Date: Thu, 04 Jun 2026 07:21:55 GMT
• Title: VISTA: Vision-Grounded and Physics-Validated Adaptation of UMI data for VLA Training
• Abstract summary: Universal Manipulation Interface (UMI) enables scalable real-world robot data collection without hardware-specific teleoperation.<n>We present VISTA, a framework that bridges this dual gap through three synergistic components.<n>We release the physical-validation pipeline, UMI-VQA, validated trajectory data, and the pre-trained model for the community.
• Score: 52.05483137072975
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Closing the Lab-to-Store Gap: A Data-Efficient Post-Training and Experience-Driven Learning VLA Framework for Retail Humanoids [0.14680035572775532]
  This paper presents a systems-level approach evaluated on a supermarket chip-restocking task using a Unitree G1-Edu humanoid robot and the GR00T N1.6 foundation model.<n>Deed comprises three key components: (1) a data-efficient post-training pipeline with control-frequency alignment, data curation, task-relevant visual highlighting, and reduced VLA dependence; (2) a real-world study of experience-driven refinement, adapted from RECAP via a text-based advantage prefix and a vision-language value function; and (3) a latent-space analysis tool for studying in- and out-of-distribution behavior
  arXiv  Detail & Related papers  (2026-07-22T16:30:51Z)
• ForgeVLA: Federated Vision-Language-Action Learning without Language Annotations [22.409577952899856]
  Vision-Language-Action (VLA) models hold great promise for general-purpose robotic intelligence.<n> ForgeVLA learns VLA models from distributed vision-action pairs without centralizing raw data or requiring manual annotations.
  arXiv  Detail & Related papers  (2026-05-08T09:20:56Z)
• Universal Pose Pretraining for Generalizable Vision-Language-Action Policies [83.39008378156647]
  Existing Vision-Language-Action (VLA) models often suffer from feature collapse and low training efficiency.<n>We propose Pose-VLA, a decoupled paradigm that separates VLA training into a pre-training phase for extracting universal 3D spatial priors.<n>Our framework follows a two-stage pre-training pipeline, establishing fundamental spatial grounding via poses followed by motion alignment.
  arXiv  Detail & Related papers  (2026-02-23T11:00:08Z)
• Rethinking Visual-Language-Action Model Scaling: Alignment, Mixture, and Regularization [65.37179698521766]
  Vision-Language-Action (VLA) models show strong promise for generalist robot control.<n>It remains unclear whether -- and under what conditions -- the standard "scale data" recipe translates to robotics.<n>We present a systematic, controlled study of VLA scaling that revisits core training choices for pretraining across diverse robots.
  arXiv  Detail & Related papers  (2026-02-10T12:25:43Z)
• LIBERO-X: Robustness Litmus for Vision-Language-Action Models [32.29541801424534]
  This work systematically rethinks VLA benchmarking from both evaluation and data perspectives.<n>We introduce LIBERO-X, a benchmark featuring a hierarchical evaluation protocol with progressive difficulty levels targeting three core capabilities.<n> Experiments with representative VLA models reveal significant performance drops under cumulative perturbations.
  arXiv  Detail & Related papers  (2026-02-06T09:59:12Z)
• Reshaping Action Error Distributions for Reliable Vision-Language-Action Models [69.38615670891038]
  In robotic manipulation, vision-language-action (VLA) models have emerged as a promising paradigm for learning generalizable and scalable robot policies.<n>We focus on continuous-action VLA models and move beyond conventional MSE-based regression by reshaping action error distributions during training.<n>We evaluate our approaches across standard, few-shot, and noisy settings on multiple representative VLA architectures.
  arXiv  Detail & Related papers  (2026-02-04T05:37:09Z)
• iFlyBot-VLA Technical Report [25.330744626382977]
  We introduce iFlyBot-VLA, a large-scale Vision-Language-Action (VLA) model trained under a novel framework.<n>The main contributions are listed as follows: (1) a latent action model thoroughly trained on large-scale human and robotic manipulation videos; (2) a dual-level action representation framework that jointly supervises both the Vision-Language Model (VLM) and the action expert during training; and (3) a mixed training strategy that combines robot trajectory data with general QA and spatial QA datasets.
  arXiv  Detail & Related papers  (2025-11-01T06:24:56Z)
• Continual Learning for VLMs: A Survey and Taxonomy Beyond Forgetting [70.83781268763215]
  Vision-language models (VLMs) have achieved impressive performance across diverse multimodal tasks by leveraging large-scale pre-training.<n>VLMs face unique challenges such as cross-modal feature drift, parameter interference due to shared architectures, and zero-shot capability erosion.<n>This survey aims to serve as a comprehensive and diagnostic reference for researchers developing lifelong vision-language systems.
  arXiv  Detail & Related papers  (2025-08-06T09:03:10Z)
• Towards Scale Consistent Monocular Visual Odometry by Learning from the Virtual World [83.36195426897768]
  We propose VRVO, a novel framework for retrieving the absolute scale from virtual data. We first train a scale-aware disparity network using both monocular real images and stereo virtual data. The resulting scale-consistent disparities are then integrated with a direct VO system.
  arXiv  Detail & Related papers  (2022-03-11T01:51:54Z)
• Unsupervised Vision-and-Language Pre-training Without Parallel Images and Captions [92.47566804182338]
  We investigate if a strong V&L representation model can be learned through unsupervised pre-training without image-caption corpora. In particular, we propose to conduct mask-and-predict'' pre-training on text-only and image-only corpora. We find that such a simple approach performance close to a model pre-trained with aligned data, on four English V&L benchmarks.
  arXiv  Detail & Related papers  (2020-10-24T08:17:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.