WaveletkAN Pose Refinement for Robot Bin Picking Driven by Cluttered RGB-D Point Clouds

Abstract

Grasping cluttered boxes is still limited by the gap between rough 6-degree-of-freedom perception and the millimeter-level alignment required for robot execution. WaveletkAN is a pose-refinement model based on RGB-D point clouds proposed in this paper to correct object pose hypotheses for dense bins with occlusion, specular depth noise, and self-similar industrial parts. The method represents each candidate as a local observed point set, a CAD-derived canonical set, and a compact bin-context tensor, then predicts a residual rigid motion via Kolmogorov-Arnold network layers parametrized by learnable wavelet atoms. A confidence-weighted correspondence field and a residual SE (3) update can be used with the traditional proposal generator and grasp planner. WaveletkAN reduced the median translation error from 7.8 mm to 2.9 mm and the median rotation error from 5.6 degrees to 1.9 degrees after refinement in a simulated-real mixed benchmark with 18 object categories and 42,600 evaluated hypotheses. The successful pick rate in dense clutter rose to 93.1 %, and the mean refinement latency of the industrial GPU was still 11.6 ms. Ablation experiments showed that removing the wavelet basis increased ADD-S by 31.7%, and omitting context gating reduced top-1 executable pose recall by 6.8 %. Based on the above experiments, multi-scale functional parameterization can achieve robust and deployable pose correction for RGB-D robotic bin-picking systems.