论文/arXiv 2608.22296·2 个月前

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

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。

AI 整理

• 本体 SDK 与控制
• 感知与导航
• 数据与采集

原文链接：TONAV: Task-Oriented Navigation and Action-Velocity Chunk Learning for Articulated Object Quadrupedal Mobile Manipulation（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

来源

本页事实对应的公开页面，以原文为准。

1. arXiv 2608.22296：TONAV: Task-Oriented Navigation and Action-Velocity Chunk Learning for Articulated Object Quadrupedal Mobile Manipulation访问 2026-10-21