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

arXiv: https://arxiv.org/abs/2608.22296 · HTML: https://mlsanity.com/paper/2608.22296/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 43 references that are in the map.
- First posted on arXiv 23 Aug 2026.

## Builds on (most cited first, 4 of 4)

- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [N2M: Bridging Navigation and Manipulation by Learning Pose Preference from Rollout](https://mlsanity.com/paper/2509.18671/) (2025)
- [UniLM-Nav: A Unified Framework for Zero-Shot Last-Mile Navigation](https://mlsanity.com/paper/2607.06537/) (2026)
- [SigLoMa: Learning Open-World Quadrupedal Loco-Manipulation from Ego-Centric Vision](https://mlsanity.com/paper/2605.03846/) (2026)

## Abstract (arXiv)

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.