A Generalized Placeability Metric for Model-Free Unified Pick-and-Place Reasoning

To reliably pick and place unknown objects under real-world sensing noise remains a challenging task, as existing methods rely on strong object priors (e.g., CAD models), or planar-support assumptions, limiting generalization and unified reasoning between grasping and placing. In this work, we introduce a generalized placeability metric that evaluates placement poses directly from noisy point clouds, without any shape priors. The metric jointly scores stability, graspability, and clearance. From raw geometry, we extract the support surfaces of the object to generate diverse candidates for multi-orientation placement and sample contacts that satisfy collision and stability constraints. By conditioning grasp scores on each candidate placement, our proposed method enables model-free unified pick-and-place reasoning and selects grasp-place pairs that lead to stable, collision-free placements. On unseen real objects and non-planar object supports, our metric delivers CAD-comparable accuracy in predicting stability loss and generally produces more physically plausible placements than learning-based predictors.