まず転ばないこと：損傷したヒューマノイドロボットが壁を活用する学習
First do not fall: learning to exploit a wall with a damaged humanoid robot
壁の向き・距離・姿勢から手を突く位置をニューラルネットで瞬時に選び、全身制御で安定姿勢を取るD-Reflexを提案。シミュレーションのTALOSで回避可能な転倒の75%以上を防ぎ、実機でも動作することを示した。
著者: Timothée Anne, Eloïse Dalin, Ivan Bergonzani, Serena Ivaldi, Jean-Baptiste Mouret
分類: cs.RO, cs.AI
原文アブストラクト
Humanoid robots could replace humans in hazardous situations but most of such situations are equally dangerous for them, which means that they have a high chance of being damaged and falling. We hypothesize that humanoid robots would be mostly used in buildings, which makes them likely to be close to a wall. To avoid a fall, they can therefore lean on the closest wall, as a human would do, provided that they find in a few milliseconds where to put the hand(s). This article introduces a method, called D-Reflex, that learns a neural network that chooses this contact position given the wall orientation, the wall distance, and the posture of the robot. This contact position is then used by a whole-body controller to reach a stable posture. We show that D-Reflex allows a simulated TALOS robot (1.75m, 100kg, 30 degrees of freedom) to avoid more than 75% of the avoidable falls and can work on the real robot.
関連論文
- ヒューマノイドの転倒回避可能性を監視する能動的到達可能性学習ヒューマノイド/転倒回避
