Grasp It Like a Pro 3.0: An Expert-Based Data-Driven Algorithm for Grasping Unknown Objects in Cluttered Environments

Grasping unknown objects in clutter remains challenging due to partial occlusions, self-occlusions, and frequent object interactions during execution. In this paper we present Grasp It Like a Pro 3.0 (GILP 3.0), a lightweight learning-from-demonstration pipeline for closed-loop clutter clearing with unknown objects. The method segments the scene from raw RGB-D point clouds, approximates candidate regions via MVBB decomposition, and predicts grasp pose and interaction wrench from compact MVBB descriptors using two histogram-based gradient-boosted decision-tree regressors trained from a limited number of human demonstrations. Grasp candidates are ranked with execution-oriented feasibility and collision-aware scoring, and the pipeline iteratively reacquires and replans after each attempt to handle object motion and occlusions in clutter. Real-robot experiments on a Franka Emika Panda with Franka Hand in 12 cluttered scenes (47 objects) achieve 95.7% perobject success and a 91.7% scene clearing rate, demonstrating a replicable and data-efficient solution for grasping unknown everyday objects in cluttered environments.