# RoboDexVLM框架实现协作臂灵巧手零样本长程任务规划与抓取

> 原文链接：https://robotics.hellobike.com/papers/2025-03-03-2503-01616-b5e35da5
> 频道：papers | 更新日期：2025-03-03 | 标签：auto, arxiv, cs.RO
> 核心摘要：针对协作机械臂配灵巧手场景，提出RoboDexVLM框架。集成VLM任务级恢复机制与语言引导抓取感知算法，支持自然语言指令下的零样本开放词汇操作，验证了长程复杂任务执行的有效性与鲁棒性，项目已开源。

# RoboDexVLM框架实现协作臂灵巧手零样本长程任务规划与抓取

::: v-pre
> 本文由 AI 根据公开内容整理，可能有误，请以原文为准：[RoboDexVLM: Visual Language Model-Enabled Task Planning and Motion Control for Dexterous Robot Manipulation](https://arxiv.org/abs/2503.01616)（arXiv 2503.01616）

## 核心内容

arXiv论文2503.01616发布RoboDexVLM框架，专为配备灵巧手的协作机械臂设计，解决现有方法在长程任务中难以处理多样化物体抓取的局限。该系统支持基于自然语言指令的开放词汇操作，通过任务级恢复机制提升长序列任务执行的鲁棒性，并在复杂环境实验中验证了其在零样本灵巧操作中的适应性与有效性，相关代码与文档已在GitHub开源。

技术架构包含两大核心：一是基于视觉语言模型（VLM）的任务规划器，具备任务级错误恢复能力，可解析并执行开放词汇长序列指令；二是结合机器人运动学与形式化方法的语言引导灵巧抓取感知算法，实现无需额外训练的零样本抓取。系统通过端到端整合语义理解与底层控制，使协作臂能在非结构化环境中根据自然语言完成多步骤灵巧操作，突破了传统方法对预定义动作空间的依赖。

## 关键看点

- **VLM任务恢复**：利用视觉语言模型构建任务级恢复机制，支持开放词汇长序列指令解析与容错执行
- **零样本抓取感知**：融合运动学与形式化方法的语言引导算法，实现无训练数据的多样化物体灵巧抓取
- **长程操作验证**：实验证实框架在复杂环境下执行多步骤自然语言指令任务的有效性与鲁棒性
- **开源资产释放**：完整项目代码与技术文档已通过GitHub开源，支持社区复现与二次开发

## 工程与产业观察

该工作将VLM语义推理与形式化运动学约束结合，为灵巧手提供了一条兼顾开放词汇理解与物理可行性的零样本操作路径。其任务级恢复机制显著提升了长程任务的工程可用性，开源代码为具身智能研究中“语言-动作”对齐提供了可复现基准，对降低灵巧操作数据依赖具有参考价值。

## 英文摘要 (Abstract)

This paper introduces RoboDexVLM, an innovative framework for robot task planning and grasp detection tailored for a collaborative manipulator equipped with a dexterous hand. Previous methods focus on simplified and limited manipulation tasks, which often neglect the complexities associated with grasping a diverse array of objects in a long-horizon manner. In contrast, our proposed framework utilizes a dexterous hand capable of grasping objects of varying shapes and sizes while executing tasks based on natural language commands. The proposed approach has the following core components: First, a robust task planner with a task-level recovery mechanism that leverages vision-language models (VLMs) is designed, which enables the system to interpret and execute open-vocabulary commands for long sequence tasks. Second, a language-guided dexterous grasp perception algorithm is presented based on robot kinematics and formal methods, tailored for zero-shot dexterous manipulation with diverse objects and commands. Comprehensive experimental results validate the effectiveness, adaptability, and robustness of RoboDexVLM in handling long-horizon scenarios and performing dexterous grasping. These results highlight the framework's ability to operate in complex environments, showcasing its potential for open-vocabulary dexterous manipulation. Our open-source project page can be found at https://henryhcliu.github.io/robodexvlm.

作者：Haichao Liu, Sikai Guo, Pengfei Mai, Jiahang Cao, Haoang Li, Jun Ma
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## 原始参考来源 (Sources)

- [arXiv 2503.01616：RoboDexVLM: Visual Language Model-Enabled Task Planning and Motion Control for Dexterous Robot Manipulation](https://arxiv.org/abs/2503.01616)