Poster presentation in the Workshop on Manipulation Robustness: Towards Human-Level Robustness under Real-World Challenges

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Robust grasping in cluttered, unstructured environments remains challenging for mobile legged manipulators due to occlusions, unreliable depth, and the need for collision-free, execution-feasible approaches. We present an end-to-end pipeline for language-guided grasping that bridges open-vocabulary target selection to safe grasp execution on a real robot. Given a natural-language command, the system grounds the target in RGB using open-vocabulary detection and promptable segmentation, extracts an object-centric point cloud from RGB-D, and improves geometric reliability under occlusion via back-projected depth compensation and two-stage point cloud completion. We then generate and collision-filter 6-DoF grasp candidates and select an executable grasp using safety-oriented heuristics that account for reachability, approach feasibility, and clearance. We evaluate the method on a Boston Dynamics Spot with an arm in two cluttered tabletop scenarios, using paired trials against a view-dependent basel