TONAV: 関節物体の四足移動操作のためのタスク指向ナビゲーションと動作速度チャンク学習
TONAV: Task-Oriented Navigation and Action-Velocity Chunk Learning for Articulated Object Quadrupedal Mobile Manipulation
四足ロボットによる関節物体の移動操作を統合するフレームワークを提案。タスク指向ナビゲーションと動作速度チャンク学習を組み合わせ、ナビゲーションと操作のギャップを解消し、安定した連続接触操作を実現した。
著者: Haoran Lin, Mingyu Yang, Pengfei Qi, Kehan Chen, Qiang Diao, Liangji Zeng, Wenrui Chen, Yaonan Wang, Kailun Yang
分類: cs.RO, cs.CV
原文アブストラクト
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.