Beyond Arrival: Geometric Alignment for Agentic Humanoid Loco-manipulation

Loco-manipulation for humanoid robots in open environments requires both long-range navigation and whole-body loco-manipulation abilities. While recent works have demonstrated powerful navigation and manipulation capabilities, bridging the two within a unified framework remains an open problem. The primary difficulty is that the navigation goal and the initial state of manipulation are misaligned in terms of observations. Existing approaches typically rely on task-specific training or additional annotations, with limited generalization across tasks and scenes. We introduce a handoff phase to reduce observation misalignment and use object-relative geometry as a bridge between navigation and manipulation. Building upon this insight, we propose GATE, a Geometry Aligned Task Execution bridge that leverages geometry aware representations to bridge navigation and manipulation, requiring no extra training or markers. An agent selects position and orientation references from the manipulation objective; a geometry aware feedback controller adjusts the robot's position before handoff. Our approach effectively improves the overall success rate of loco-manipulation tasks and demonstrates clear advantages over alternative methods in generalization, stability, and robustness. Building upon this approach, we develop an agentic system and deploy it in the real world. It completes multiple complex whole-body manipulation tasks with substantially higher success rates than the baselines. We further evaluate GATE on the OVMM benchmark, providing evidence that the geometric handoff principle is applicable to a different embodiment and an independently trained manipulation policy.