SOURCE-LINKED INTELLIGENCE
DeViGrasp: Robust Visual Mobile Grasping for Quadruped Manipulators under Degraded Perception
Quadruped manipulators enable mobile grasping in complex environments, yet their whole-body control policies remain vulnerable to unreliable onboard visual perception. Existing methods are typically developed under relatively reliable observations and have not systematically examined how occlusion, segmentation-mask dropout, depth noise, and target-localization jitter affect grasp reasoning and target tracking. To address this gap, we introduce DeViGrasp-Bench, a benchmark for mobile grasping under degraded vision that incorporates controlled visual degradations, seen and unseen objects, multi
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-12T12:44:24.000Z
First collected: 2026-09-24T14:02:34.369Z. This is not the publication date.