Optimizing Robotic Placement via Grasp-Dependent Feasibility Prediction

• URL: http://arxiv.org/abs/2512.18922v1
• Date: Sun, 21 Dec 2025 23:47:09 GMT
• Title: Optimizing Robotic Placement via Grasp-Dependent Feasibility Prediction
• Abstract summary: We generate two path-aware labels: path-wise inverse kinematics (IK) feasibility across a fixed approach-grasp-lift waypoint template, and a collision flag from mesh sweeps along the same template.<n>A compact dual-output learns these signals from pose encodings, and at test time its scores rank precomputed candidates for a rank-then-plan policy under the same IK gate and planner as the baseline.<n>Although learned from cheap labels only, the scores transfer to physics-enabled executed trajectories: at a fixed planning budget the policy finds successful paths sooner with fewer planner calls while keeping final success on
• Score: 2.682400665831876
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we study whether inexpensive, physics-free supervision can reliably prioritize grasp-place candidates for budget-aware pick-and-place. From an object's initial pose, target pose, and a candidate grasp, we generate two path-aware geometric labels: path-wise inverse kinematics (IK) feasibility across a fixed approach-grasp-lift waypoint template, and a transit collision flag from mesh sweeps along the same template. A compact dual-output MLP learns these signals from pose encodings, and at test time its scores rank precomputed candidates for a rank-then-plan policy under the same IK gate and planner as the baseline. Although learned from cheap labels only, the scores transfer to physics-enabled executed trajectories: at a fixed planning budget the policy finds successful paths sooner with fewer planner calls while keeping final success on par or better. This work targets a single rigid cuboid with side-face grasps and a fixed waypoint template, and we outline extensions to varied objects and richer waypoint schemes.

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