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Grasp Execution Without a Planner: Configuration-Space Grasp Distance Fields with Certified Safety & Guaranteed Quality

2026/08/01 by Clinton Enwerem, John S. Baras, Calin Belta
Computer Science · Engineering · Mathematics · #cs.RO #cs.SY #eess.SY #math.OC

paper · pdf

14 pages, 7 figures, 3 tables. Project page: www.clintonenwerem.com/gdf

arxiv created 2026/08/04 · arxiv updated 2026/08/05

Abstract

Standard multifingered grasp execution architectures plan a collision-free trajectory to a selected grasp pose and track it with a feedback law. Execution-time object pose uncertainty or perturbations may invalidate the planned trajectory, forcing a costly replanning step. We present Grasp Distance Fields (GDFs), smooth softmin distance fields over grasp configurations in the arm-hand configuration space. Our controller executes a grasp by following the negative GDF gradient with a stationary feedback law, eliminating the need for a planner, stored trajectory, or grasp selection. For safety, we filter the resulting command through a CBF-CLF quadratic program (QP), constraining self-collision, workspace, object, and obstacle clearance, reporting impeded progress as explicit slack. We prove that the softmin tracks the true set distance within log N/ρ for N candidates and smoothing parameter ρ, and that the filtered closed loop renders the safe set forward invariant. Since no smooth field captures hand-object contact switching, we switch between hand closure and grasp retention modes using hysteresis at a pregrasp configuration, trading object collision avoidance for contact admission. A wrench-quality CBF then bounds the realized grasp's force-closure margin within a prescribed tolerance of its value at hold onset. We evaluate in kinematic simulation on a fixed-base arm and a Unitree G1 humanoid, both fitted with the same underactuated hand. Our controller navigates cluttered and dynamic scenes to safely reach for, grasp, and lift 46 of 50 test objects spanning primitive, household, and adversarial classes. Across the 46 lifts, the executed grasps retain a median 94% of their synthesized quality margin, at 0.09 ms QP solve time per 20 ms control step. Project page: www.clintonenwerem.com/gdf.

Citations