Organizations: School of Artificial Intelligence and Robotics, Hunan University, China · National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University, China · College of Semiconductors (College of Integrated Circuits), Hunan University, Changsha, China
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.
Figures & tables
Fig. 1 : Illustration of articulated-object quadrupedal mobile manipulation, with progressive target approaching followed by position-velocity-based sustained-contact manipulation for smoother and more stable interaction.
Fig. 2 : Comparison of position-only and position-velocity-coupled teleoperation. (a) Teleoperation at increasing speeds, showing reduced tracking lag with position-velocity coupling. (b) Master-follower trajectories at 1× and 2× speeds, showing lower latency and better temporal consistency.
Fig. 3 : Position-velocity-coupled teleoperation with 220Hz position-velocity tracking, 30Hz camera observations, and timestamp-based alignment across latency-varying modalities.
Fig. 4 : Overview of TONAV with an upper navigation branch and a lower manipulation branch. The upper branch combines hierarchical task reasoning and adaptive last-mile navigation to reach a manipulation-ready configuration, while the lower branch learns action-velocity chunks from motion-consistent demonstrations for smooth and stable sustained-contact manipulation.
Fig. 5 : Illustration of the Adaptive Approach Module (AAM). AAM performs coarse approaching with a diffusion policy and fine target refinement with visual feedback.
Fig. 6 : Qualitative comparison on the two tasks. Ours leverages action–velocity demonstrations and chunk learning to achieve smoother motion and more stable sustained-contact manipulation than ACT [ 28 ] and DP [ 29 ] .
Navigation Success Rates
Overall
Method
Close Drawer
Turn On Lamp
Lower Toilet Lid
SR
StreamVLN [ 7 ]
0/5
1/5
1/5
13.33%
InternNav [ 43 ]
2/5
1/5
1/5
26.67%
TONAV (Ours)
4/5
3/5
2/5
60.00%
TABLE I : Comparison of navigation Success Rates (SR) in the first stage across three articulated-object loco-manipulation tasks, together with the Overall SR.
Fig. 7 : Qualitative comparison of task-oriented navigation. Ours progressively approaches the target, refines the base pose for manipulation readiness, and closes the drawer within the manipulable workspace using AVTF-based control (see Sec. III-A ).
Fig. 8 : Joint-angle trajectories of Piper Joints 1–3 during the Close Drawer task. Ours yields smoother trajectories with reduced oscillation than ACT [ 28 ] and DP [ 29 ] .
Fig. 9 : Real-world demonstration of AAM. The first two frames show coarse approach, and the last two refine base orientation and target distance for manipulation readiness.
Task Success Rates
Overall
Method
Close Drawer
Turn On Lamp
Lower Toilet Lid
SR
ACT [ 28 ] (w/o P-V Control)
1/5
0/5
1/5
13.33%
DP [ 29 ] (w/o P-V Control)
1/5
1/5
0/5
13.33%
TONAV (w/o P-V Control)
2/5
2/5
4/5
53.33%
ACT [ 28 ]
2/5
3/5
2/5
46.67%
DP [ 29 ]
2/5
1/5
1/5
26.67%
TABLE II : Comparison of complete mobile manipulation success rates, with all methods sharing a single navigation run to the manipulation-ready region. ( w/o P-V Control : demonstrations collected without position-velocity-coupled teleoperation.)
Instruction Decomposition Success Rates
Overall
Method
Close Drawer
Turn On Lamp
Lower Toilet Lid
SR
Doubao-Seed-2.1-Pro (w/o PP-CoT)
1/5
1/5
0/5
13.33%
Qwen-3.7-Max (w/o PP-CoT)
2/5
1/5
1/5
26.67%
Doubao-Seed-2.1-Pro
3/5
2/5
2/5
46.67%
Qwen-3.7-Max (Ours)
4/5
3/5
2/5
60%
TABLE III : Ablation of PP-CoT and comparison of different LLMs for manipulation-oriented navigation across three articulated-object mobile manipulation tasks.