arXiv subjects

Yage He

Publications and source records attributed to Yage He.

3 recordsLinked to original sources

arXiv2026

WAVE-Go: World-Model Navigation with Adaptive Execution for Wheel-Legged Robots

World models can anticipate the consequences of navigation actions, but predicted action sequences may become invalid during execution, especially when wheel-legged robots encounter dynamic obstacles or change locomotion modes. We propose WAVE-Go, an image-goal navigation framework that separates world-action prediction from interruptible command execution. Its executor adaptively selects an action prefix and cancels pending commands when updated observations invalidate execution. A conditional-risk formulation specifies prefix selection under an estimated cumulative failure budget, while posture and locomotion-mode transitions require clearance, stability, and task-evidence checks. In the reported navigation evaluation, WAVE-Go achieves 74.1% in-distribution success and 63.3% dynamic out-of-distribution success, exceeding the strongest baseline by 4.7 and 7.7 percentage points, respectively, while reducing collisions from 4.4 to 2.9 per 100 m. Compared with interruptible fixed four-command execution, WAVE-Go raises success by 4.0 percentage points while reducing replanning frequency by 51.2% and collision rate by 6.5%. Execution ablations also show that runtime interruption improves success, collision rate, and reaction latency at the cost of additional replanning. These results support adaptive, interruptible execution as a means of balancing navigation performance and planning overhead. Code is available at https://github.com/vigorlee/wave-go.

cs.RO↗

arXiv2026

WAVE-Stereo: Warp-Aligned Volume Encoding for Stereo Matching

Existing iterative stereo matching methods primarily adopt two types of correspondence representation: explicit matching search via correlation volumes and local residual refinement via warped features, yet the two remain separately modeled. We propose WAVE-Stereo, built on a core insight: correlation volumes and feature warping provide complementary matching cues. \textbf{GeoWarp Correspondence Encoder (GWCE)} encodes matching search, residual alignment, and disparity prior in parallel at the ConvGRU input. To mitigate matching degradation in textureless regions, we propose \textbf{Periodic Global Context Propagation (PGCP)}, which propagates global spatial information in a periodic manner. On five real-world benchmarks -- Middlebury, ETH3D, KITTI 2012, KITTI 2015, and Booster -- WAVE-Stereo achieves competitive zero-shot generalization accuracy without any external foundation model prior, achieving 3.18\% D1-all on KITTI 2015, 4.42\% Bad-2.0 on Booster, and 66ms real-time inference, striking a favorable balance between accuracy and efficiency. Our code is available at https://github.com/yamanoko-do/WAVE-Stereo.

cs.CV↗

arXiv2025

Foundation Model-Driven Grasping of Unknown Objects via Center of Gravity Estimation

This study presents a grasping method for objects with uneven mass distribution by leveraging diffusion models to localize the center of gravity (CoG) on unknown objects. In robotic grasping, CoG deviation often leads to postural instability, where existing keypoint-based or affordance-driven methods exhibit limitations. We constructed a dataset of 790 images featuring unevenly distributed objects with keypoint annotations for CoG localization. A vision-driven framework based on foundation models was developed to achieve CoG-aware grasping. Experimental evaluations across real-world scenarios demonstrate that our method achieves a 49\% higher success rate compared to conventional keypoint-based approaches and an 11\% improvement over state-of-the-art affordance-driven methods. The system exhibits strong generalization with a 76\% CoG localization accuracy on unseen objects, providing a novel solution for precise and stable grasping tasks.

cs.RO↗