ガウス過程と位置ベースダイナミクスによる変形可能表面の能動的知覚とモデリング
Active Perception and Modeling of Deformable Surfaces using Gaussian Processes and Position-based Dynamics
少数の物理的接触から異種弾性表面の変形性分布マップをオンラインで推定する確率的フレームワークを提案。ガウス過程で環境形状をモデル化し、位置ベースダイナミクスシミュレータと回帰で局所変形性を全体に拡張する。
著者: Sergio Caccamo, Püren Güler, Hedvig Kjellström, Danica Kragic
分類: cs.RO
原文アブストラクト
Exploring and modeling heterogeneous elastic surfaces requires multiple interactions with the environment and a complex selection of physical material parameters. The most common approaches model deformable properties from sets of offline observations using computationally expensive force-based simulators. In this work we present an online probabilistic framework for autonomous estimation of a deformability distribution map of heterogeneous elastic surfaces from few physical interactions. The method takes advantage of Gaussian Processes for constructing a model of the environment geometry surrounding a robot. A fast Position-based Dynamics simulator uses focused environmental observations in order to model the elastic behavior of portions of the environment. Gaussian Process Regression maps the local deformability on the whole environment in order to generate a deformability distribution map. We show experimental results using a PrimeSense camera, a Kinova Jaco2 robotic arm and an Optoforce sensor on different deformable surfaces.
