# Vela模型采用自适应样条参数化突破VLA动作表征瓶颈

> 原文链接：https://robotics.hellobike.com/papers/2026-10-04-2610-05230
> 频道：papers | 更新日期：2026-10-04 | 标签：auto, arxiv, hf-daily-papers
> 核心摘要：针对VLA模型固定动作块导致的时序分辨率僵化问题，Vela提出基于样条的连续轨迹表征与运动依赖时间支撑机制。该模型在LIBERO-X、EBench及煎蛋饼、土豆丝等真实长程任务中验证了连续动作表征对提升操作精度的有效性。

# Vela模型采用自适应样条参数化突破VLA动作表征瓶颈

::: v-pre
> 本文由 AI 根据公开内容整理，可能有误，请以原文为准：[Vela: Scaling Vision-Language-Action Models with Adaptive Action Curve Parametrization](https://arxiv.org/abs/2610.05230)（HF Daily Papers · arXiv 2610.05230）

## 核心内容

现有视觉-语言-动作（VLA）模型多采用固定速率动作块表征未来运动，导致输出预算受限，难以兼顾长时域覆盖与接触式操作所需的局部精度。Vela模型通过引入连续轨迹表征替代离散点预测，解决了相邻动作高度相关造成的容量浪费问题，并在LIBERO-X、EBench仿真基准及煎蛋饼、土豆丝两项真实世界长程操作任务中取得优异性能，验证了连续动作表征作为具身基础模型底座的潜力。

技术上，Vela结合紧凑的样条动作表征与运动依赖的时间支撑机制，使固定输出预算能根据运动特性自适应调整时序分辨率，并设计了共享动作接口以适配异构本体。该方法将轨迹平滑性从隐式学习转为显式建模，有效缓解了传统Pointwise表征在长视野与高精度间的权衡困境。研究团队已在项目页开源相关成果，为下一代具身智能模型提供了新的动作空间建模范式。

## 关键看点

- **连续轨迹表征**：采用样条曲线替代固定速率动作块，消除相邻动作冗余，显式保证时序连续性
- **自适应时序分辨率**：引入运动依赖时间支撑机制，使固定输出预算随运动快慢动态分配采样密度
- **异构本体兼容**：设计共享动作接口统一多形态机器人输出空间，支持大规模多本体数据联合预训练
- **真实任务验证**：在煎蛋饼与土豆丝等接触密集长程操作中表现优异，证实连续表征优于离散Chunking

## 工程与产业观察

Vela证明了将机器人动作空间从离散Token回归连续函数表征的工程价值。对于具身算法工程师，样条参数化不仅降低了高频控制数据的存储与推理开销，更通过结构化归纳偏置提升了接触丰富任务的泛化能力。这提示行业在追求大模型Scaling Law的同时，应重新审视动作空间本身的数学表达效率，而非单纯堆叠Transformer层数。

## 英文摘要 (Abstract)

Most vision-language-action models represent future motion as fixed-rate action chunks, tying temporal resolution and prediction horizon to a fixed output budget. This pointwise representation wastes capacity on highly correlated neighboring actions, leaves temporal continuity and smoothness to be learned implicitly, and forces a tradeoff between long-horizon coverage and the local precision required for contact-rich manipulation. To address these limitations, we introduce Vela, a vision-language-action foundation model that represents future robot behavior as continuous trajectories. Vela combines a compact spline-based action representation with motion-dependent temporal support and a shared action interface for heterogeneous embodiments, allowing a fixed output budget to adapt its temporal resolution across motions. We pretrain Vela on large-scale multi-embodiment robot data and evaluate it on LIBERO-X, EBench, and two real-world long-horizon tasks, egg-cake cooking and potato shredding, obtaining promising results across simulation and physical manipulation. These results highlight the potential of continuous action representations as a foundation for future embodied foundation models. Project page and more results: https://clementine24.github.io/Vela/ .

Hugging Face Daily Papers 热度：4 upvotes
作者：Yifan Li, Jiaxu Wang, Dongming Wu, Yicheng Jiang, Ryan Ji, Xiangyu Yue, Yanwei Fu
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## 原始参考来源 (Sources)

- [HF Daily Papers · arXiv 2610.05230：Vela: Scaling Vision-Language-Action Models with Adaptive Action Curve Parametrization](https://arxiv.org/abs/2610.05230)