GPISとベイズ最適化を用いたCADモデルに基づく多品種少量生産向け把持計画
Grasp Planning for Flexible Production with Small Lot Sizes based on CAD models using GPIS and Bayesian Optimization
物体形状をGPISでパラメータ化し、ベイズ最適化とADMMによる二段階最適化で多指ハンドの把持計画を生成する手法を提案。シミュレータで95%の成功率を達成した。
著者: Jianjie Lin, Markus Rickert, Alois Knoll
分類: cs.RO
原文アブストラクト
Grasp planning for multi-fingered hands is still a challenging task due to the high nonlinear quality metrics, the high dimensionality of hand posture configuration, and complex object shapes. Analytical-based grasp planning algorithms formulate the grasping problem as a constraint optimization problem using advanced convex optimization solvers. However, these are not guaranteed to find a globally optimal solution. Data-driven based algorithms utilize machine learning algorithm frameworks to learn the grasp policy using enormous training data sets. This paper presents a new approach for grasp generation by formulating a global optimization problem with Bayesian optimization. Furthermore, we parameterize the object shape utilizing the Gaussian Process Implicit Surface (GPIS) to integrate the object shape information into the optimization process. Moreover, a chart defined on the object surface is used to refine palm pose locally. We introduced a dual optimization stage to optimize the palm pose and contact points separately. We further extend the Bayesian optimization by utilizing the alternating direction method of multipliers (ADMM) to eliminate contact optimization constraints. We conduct the experiments in the graspit! Simulator that demonstrates the effectiveness of this approach quantitatively and qualitatively. Our approach achieves a 95% success rate on various commonly objects with diverse shapes, scales, and weights
