# WOLF-VLA框架融合全身最优控制与VLA实现人形机器人指令驱动运动

> 原文链接：https://robotics.hellobike.com/papers/2026-06-24-2606-25591-6968cc56
> 频道：papers | 更新日期：2026-06-24 | 标签：auto, arxiv, cs.RO
> 核心摘要：针对人形机器人接触丰富运动数据稀缺痛点，提出WOLF-VLA框架。通过融合全身最优控制合成动态一致轨迹，构建六类任务多模态数据集训练VLA模型，实现自然语言指令驱动的鲁棒运动策略生成，并将开源全套资产。

# WOLF-VLA框架融合全身最优控制与VLA实现人形机器人指令驱动运动

::: v-pre
> 本文由 AI 根据公开内容整理，可能有误，请以原文为准：[WOLF-VLA: Whole-Body Humanoid Optimal Locomotion Framework for Vision-Language-Action Learning](https://arxiv.org/abs/2606.25591)（arXiv 2606.25591）

## 核心内容

针对VLA模型在人形机器人全身接触运动中应用受限的问题，研究提出WOLF-VLA统一框架。该工作整合全身最优控制（OC）运动合成与大规模多模态数据集，解决了动态一致性演示缺失及安全性编码难题。实验表明，训练出的策略在多种环境设置下具备强推理能力与对初始条件变化的鲁棒性，且性能具有竞争力。

技术层面构建了涵盖六大运动任务族的综合数据集，参数化包含环境变化、物体属性及视觉干扰项。模型输入联合轨迹、第一人称视觉观测与自然语言指令，通过系统消融实验验证了各模态贡献。团队将开源完整数据集、模型检查点及基准仿真套件，为指令驱动的人形运动策略迁移提供可复现的动态一致性基准。

## 关键看点

- **框架创新**：提出WOLF-VLA统一架构，将全身最优控制运动合成与VLA学习管线深度集成，解决人形机器人动态一致性演示数据缺失难题。
- **数据构建**：建立覆盖六类运动任务族的参数化数据集，包含环境变异、物体颜色位置及视觉干扰项，确保轨迹满足动力学可行性约束。
- **模型训练**：基于关节轨迹、第一人称视觉与自然语言指令联合训练VLA，生成的运动策略对初始条件变化表现出强鲁棒性与推理能力。
- **开源资产**：承诺开放完整数据集、模型权重及基准仿真套件，确立指令驱动人形全身运动控制的可复现动态一致性评测标准。

## 工程与产业观察

该工作填补了VLA在人形机器人全身接触运动领域的空白，通过最优控制合成数据解决了真机采集难与动力学不一致问题。开源的动态一致基准与多模态数据集，为具身大模型从操作向复杂运动拓展提供了关键基础设施，加速了指令驱动运动策略的Sim-to-Real迁移研究。

## 英文摘要 (Abstract)

Vision-Language-Action (VLA) models have recently demonstrated strong generalization in robotic manipulation, yet their applicability to whole-body, contact-rich humanoid locomotion remains severely underexplored due to data scarcity, the absence of dynamically consistent demonstrations, and the difficulty of encoding optimality and safety in learning-based pipelines. This work introduces a unified framework WOLF-VLA that integrates whole-body optimal-control (OC) motion synthesis with large-scale multi-modal dataset to train VLAs capable of generating humanoid locomotion policies directly from natural-language instructions. We construct a comprehensive dataset of dynamically feasible humanoid trajectories across six locomotion-related task families, each parameterized by environmental variations, object colors, placements, and visual distractors. We train a VLA model using the collected joint trajectories, ego-centric visual observations and natural language instruction, yielding a policy that exhibits strong reasoning and robustness to initial-condition variability, and competitive performance across several tasks and environment settings. A systematic ablation study demonstrates the impact of each modality on the model performance. The full dataset, model checkpoints, and benchmarking simulation suite will be openly released, establishing a reproducible dynamically consistent benchmark for whole-body humanoid locomotion rich VLA control and enabling future research in scalable transfer of instruction-driven locomotion policies.

作者：Melya Boukheddimi, Omar Adjali, Daniel Sontag, Frank Kirchner
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## 原始参考来源 (Sources)

- [arXiv 2606.25591：WOLF-VLA: Whole-Body Humanoid Optimal Locomotion Framework for Vision-Language-Action Learning](https://arxiv.org/abs/2606.25591)