障害物回避から動作学習へ：力学系の局所回転によるアプローチ
From Obstacle Avoidance To Motion Learning Using Local Rotation of Dynamical Systems
ロボット自身を基準にした相対的な回転で動作を記述し、星形障害物を回避しつつ力学系に沿う枠組みを提案。さらに自動クラスタリングで局所安定領域を学習し、手書きデータセットで有効性を示した。
著者: Lukas Huber, Jean-Jacques Slotine, Aude Billard
分類: cs.RO
原文アブストラクト
In robotics motion is often described from an external perspective, i.e., we give information on the obstacle motion in a mathematical manner with respect to a specific (often inertial) reference frame. In the current work, we propose to describe the robotic motion with respect to the robot itself. Similar to how we give instructions to each other (go straight, and then after multiple meters move left, and then a sharp turn right.), we give the instructions to a robot as a relative rotation. We first introduce an obstacle avoidance framework that allows avoiding star-shaped obstacles while trying to stay close to an initial (linear or nonlinear) dynamical system. The framework of the local rotation is extended to motion learning. Automated clustering defines regions of local stability, for which the precise dynamics are individually learned. The framework has been applied to the LASA-handwriting dataset and shows promising results.
