# A Robust Placeability Metric for Model-Free Unified Pick-and-Place Reasoning Source: [A Robust Placeability Metric for Model-Free Unified Pick-and-Place Reasoning]() Collection index: [llms.txt]() Data updated: 2026-08-01T05:00:13.650Z Authors: Benno Wingender, Nils Dengler, Rohit Menon, Sicong Pan, Maren Bennewitz Published: 2025-10-16T11:43:02.000Z ## Abstract Reliable manipulation of previously unseen objects remains a fundamental challenge for autonomous robotic systems operating in unstructured environments. In particular, robust pick-and-place planning directly from noisy and only partial real-world observations, where object surfaces are inherently incomplete due to occlusions (e.g., bottom faces on a tabletop), is difficult. As a result, many existing methods rely on strong object priors (e.g., CAD models) or to assume placement on continuous, flat support surfaces such as planar tabletops, without explicitly accounting for edge proximity or inclined supports. In this work, we introduce a robust probabilistic placeability metric that evaluates 6D object placement poses from partial observations by jointly scoring object stability and graspability from raw point cloud geometry. Using this metric, we generate diverse multi-orientation placement candidates and condition grasp scoring on these placements, enabling model-free unified pick-and-place reasoning. Simulation and real-robot experiments on unseen objects and challenging support geometries confirm that our metric yields accurate stability predictions and consistently improves end-to-end pick-and-place success by producing stable, collision-free grasp-place pairs directly from partial point clouds. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Stable Placement Prediction on YCB](): Stable Placement Prediction; YCB; Rotational Error (deg): 2.5. - [Pick-and-place on Cluttered Shelf Environment](): Pick-&-Place; Cluttered Shelf Environment; Success Rate (Drill): 100. - [Pick-and-place on Height-Reduced Environment](): Pick-&-Place; Height-Reduced Environment; Success Rate (Drill): 100. ## Original sources and implementation [Original paper]() [PDF]()