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RoboUniView: Visual-Language Model with Unified View Representation for Robotic Manipulation

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arxiv 2406.18977 v3 pith:S73WXJK5 submitted 2024-06-27 cs.RO cs.CLcs.CV

Fanfan Liu , Feng Yan , Liming Zheng , Chengjian Feng , Yiyang Huang , Lin Ma This is my paper
classification cs.ROcs.CLcs.CV
keywords roboticcamerarepresentationunifiedviewmanipulationmodelparameters
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abstract

Utilizing Vision-Language Models (VLMs) for robotic manipulation represents a novel paradigm, aiming to enhance the model's ability to generalize to new objects and instructions. However, due to variations in camera specifications and mounting positions, existing methods exhibit significant performance disparities across different robotic platforms. To address this challenge, we propose RoboUniView in this paper, an innovative approach that decouples visual feature extraction from action learning. We first learn a unified view representation from multi-perspective views by pre-training on readily accessible data, and then derive actions from this unified view representation to control robotic manipulation. This unified view representation more accurately mirrors the physical world and is not constrained by the robotic platform's camera parameters. Thanks to this methodology, we achieve state-of-the-art performance on the demanding CALVIN benchmark, enhancing the success rate in the $D \to D$ setting from 93.0% to 96.2%, and in the $ABC \to D$ setting from 92.2% to 94.2%. Moreover, our model exhibits outstanding adaptability and flexibility: it maintains high performance under unseen camera parameters, can utilize multiple datasets with varying camera parameters, and is capable of joint cross-task learning across datasets. Code is provided for re-implementation. https://github.com/liufanfanlff/RoboUniview

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Cited by 12 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. SkiP: When to Skip and When to Refine for Efficient Robot Manipulation

    cs.RO 2026-05 unverdicted novelty 7.0 of 10

    SkiP introduces action relabeling and Motion Spectrum Keying to skip redundant steps in robot trajectories, cutting executed steps by 15-40% while maintaining success rates across 72 simulated and 3 real tasks.

  2. CF-VLA: Efficient Coarse-to-Fine Action Generation for Vision-Language-Action Policies

    cs.CV 2026-04 unverdicted novelty 7.0 of 10

    CF-VLA uses a coarse initialization over endpoint velocity followed by single-step refinement to achieve strong performance with low inference steps on CALVIN, LIBERO, and real-robot tasks.

  3. See like a Robot: Robot-Centric Pointmaps for Vision-Language-Action Models

    cs.RO 2026-07 conditional novelty 6.0 of 10

    Expressing RGB-D observations as end-effector-centered robot-frame pointmaps and adding them element-wise to RGB tokens improves pretrained VLAs under camera viewpoint variation.

  4. Action-Effect Memory Pretraining for Robot Manipulation

    cs.RO 2026-06 unverdicted novelty 6.0 of 10

    AEM pretrains compact history representations via masked modeling on interleaved vision-action sequences to boost downstream robot manipulation in simulation and real settings.

  5. PALM: Progress-Aware Policy Learning via Affordance Reasoning for Long-Horizon Robotic Manipulation

    cs.RO 2026-01 unverdicted novelty 6.0 of 10

    PALM improves long-horizon robotic manipulation success by distilling affordance representations for object interaction and predicting within-subtask progress in a VLA model.

  6. FLOWER: Democratizing Generalist Robot Policies with Efficient Vision-Language-Action Flow Policies

    cs.RO 2025-09 conditional novelty 6.0 of 10

    A compact 950-million-parameter robot policy trained in about 200 GPU-hours matches or beats multi-billion-parameter baselines on most manipulation benchmarks, including a new best score on CALVIN ABC.

  7. Multi-Omics Analysis for Cancer Subtype Inference via Unrolling Graph Smoothness Priors

    cs.LG 2025-08 unverdicted novelty 6.0 of 10

    GTMancer unrolls multiplex graph smoothness priors with contrastive learning and dual attention to integrate multi-omics data for cancer subtype classification.

  8. Video Prediction Policy: A Generalist Robot Policy with Predictive Visual Representations

    cs.CV 2024-12 unverdicted novelty 6.0 of 10

    Video Prediction Policy conditions robot action learning on future-frame predictions inside fine-tuned video diffusion models, yielding 18.6% relative gains on Calvin ABC-D and 31.6% higher real-world success rates.

  9. A Survey on Vision-Language-Action Models for Embodied AI

    cs.RO 2024-05 unverdicted novelty 6.0 of 10

    This is the first survey on vision-language-action models, providing a taxonomy across three lines, plus summaries of datasets, simulators, benchmarks, challenges, and future directions in embodied AI.

  10. What Matters in Building Vision-Language-Action Models for Generalist Robots

    cs.RO 2024-12 unverdicted novelty 5.0 of 10

    Systematic tests of VLM backbones, policy architectures, and cross-embodiment data yield RoboVLMs that set new SOTA on robot manipulation benchmarks while requiring few manual designs.

  11. General Covariant Action Modeling: Constructing Generalized Manifolds via Spatio-Temporal Decoupling

    cs.CV 2026-05 unverdicted novelty 4.0 of 10

    GAM framework uses arc-length parameterization for temporal invariance and schema-affine factorization for geometric invariance to build a covariant action manifold integrated into VLA models for improved generalizati...

  12. Robotic Manipulation via Imitation Learning: Taxonomy, Evolution, Benchmark, and Challenges

    cs.RO 2025-08 conditional novelty 4.0 of 10

    A survey that taxonomizes robotic manipulation policies trained by imitation learning, traces their evolution, and compiles benchmark comparisons.

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