# TONAV：面向关节物体四足移动操作的任务导向导航与动作-速度块学习

> 原文链接：https://robotics.hellobike.com/papers/2026-08-23-2608-22296
> 频道：papers | 更新日期：2026-08-23 | 标签：arxiv, cs.RO
> 核心摘要：TONAV结合任务导向导航与动作-速度块学习，在真实关节物体任务中提升导航与移动操作成功率。

# TONAV：面向关节物体四足移动操作的任务导向导航与动作-速度块学习

::: v-pre
> 原文链接：[TONAV: Task-Oriented Navigation and Action-Velocity Chunk Learning for Articulated Object Quadrupedal Mobile Manipulation](https://arxiv.org/abs/2608.22296)（arXiv 2608.22296 · 发布日期：2026-08-23）

**TONAV: Task-Oriented Navigation and Action-Velocity Chunk Learning for Articulated Object Quadrupedal Mobile Manipulation**

TONAV结合任务导向导航与动作-速度块学习，在真实关节物体任务中提升导航与移动操作成功率。

## 核心要点

- 针对四足移动操作中导航止于目标附近、未达操作就绪，以及接触交互不稳定的问题。
- 提出位置-速度耦合遥操作，显式捕捉运动动态，以采集平滑且时间一致的示教。
- 利用视觉语言推理分解高层指令为可执行子目标，并自适应调整底盘至操作就绪构型。
- 真实世界多样关节物体任务表明，TONAV提升任务导向导航和完整移动操作成功率；项目页已提供。

**验证范围与局限**：验证范围为真实世界中的多样关节物体任务。

## 英文摘要 (Abstract)

Quadruped mobile manipulation requires two tightly coupled capabilities: reaching manipulation-ready configurations and maintaining stable contact throughout articulated-object interaction. However, existing methods often terminate navigation near the target, leaving a gap between reachability and manipulation readiness, while tracking lag, motion jitter, and contact instability limit continuous interaction. To address these challenges, we present TONAV, a unified framework integrating task-oriented navigation with action-velocity chunk learning. First, we introduce a position-velocity-coupled teleoperation framework that explicitly captures motion dynamics to improve master-follower consistency and collect smooth, temporally consistent demonstrations. Next, task-oriented navigation leverages vision-language reasoning to decompose high-level instructions into executable subgoals and adaptively refine the robot base toward a manipulation-ready configuration. Finally, action-velocity chunk learning jointly models joint positions and their temporal transitions under velocity supervision, enabling smooth and stable sustained-contact manipulation. Real-world experiments across diverse articulated-object tasks demonstrate that TONAV achieves higher success rates in both task-oriented navigation and complete mobile manipulation, mitigating the navigation-manipulation gap and improving continuous-contact interaction. The project page is at https://haochen611.github.io/TONAV.

作者：Haoran Lin, Mingyu Yang, Pengfei Qi, Kehan Chen, Qiang Diao, Liangji Zeng, Wenrui Chen, Yaonan Wang, Kailun Yang
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## 原始参考来源 (Sources)

- [arXiv 2608.22296：TONAV: Task-Oriented Navigation and Action-Velocity Chunk Learning for Articulated Object Quadrupedal Mobile Manipulation](https://arxiv.org/abs/2608.22296)