深層生成モデルによる動作の学習と推論
Learning and Inferring Movement with Deep Generative Model
動作計画を有向グラフィカルモデル上の学習として定式化し、深層生成モデルでデモから学習・推論する確率的手法を提案。タスク記述子や文脈情報を柔軟に取り込み、ロボット制御の力学系と組み合わせ可能であることを示した。
著者: Mingxuan Jing, Xiaojian Ma, Fuchun Sun, Huaping Liu
分類: cs.LG, cs.RO, stat.ML
原文アブストラクト
Learning and inference movement is a very challenging problem due to its high dimensionality and dependency to varied environments or tasks. In this paper, we propose an effective probabilistic method for learning and inference of basic movements. The motion planning problem is formulated as learning on a directed graphic model and deep generative model is used to perform learning and inference from demonstrations. An important characteristic of this method is that it flexibly incorporates the task descriptors and context information for long-term planning and it can be combined with dynamic systems for robot control. The experimental validations on robotic approaching path planning tasks show the advantages over the base methods with limited training data.
