変形物体の予測モデルのための逐次トポロジー表現
Sequential Topological Representations for Predictive Models of Deformable Objects
点群から変形物体のトポロジー状態を抽出し、その時間発展を予測するモデルを学習する手法を提案。シミュレーション実験で従来の計算トポロジーライブラリより高精度かつ高速な多段予測を実現した。
著者: Rika Antonova, Anastasiia Varava, Peiyang Shi, J. Frederico Carvalho, Danica Kragic
分類: cs.RO, cs.CV, cs.LG
原文アブストラクト
Deformable objects present a formidable challenge for robotic manipulation due to the lack of canonical low-dimensional representations and the difficulty of capturing, predicting, and controlling such objects. We construct compact topological representations to capture the state of highly deformable objects that are topologically nontrivial. We develop an approach that tracks the evolution of this topological state through time. Under several mild assumptions, we prove that the topology of the scene and its evolution can be recovered from point clouds representing the scene. Our further contribution is a method to learn predictive models that take a sequence of past point cloud observations as input and predict a sequence of topological states, conditioned on target/future control actions. Our experiments with highly deformable objects in simulation show that the proposed multistep predictive models yield more precise results than those obtained from computational topology libraries. These models can leverage patterns inferred across various objects and offer fast multistep predictions suitable for real-time applications.
