歩行支援デバイスの転倒防止制御ポリシーの学習
Learning a Control Policy for Fall Prevention on an Assistive Walking Device
歩行支援デバイスに搭載したセンサとアクチュエータを用い、転倒予測器と回復ポリシーを学習させて転倒を防ぐ手法を提案し、シミュレーションで有効性を示した。
著者: Visak C V Kumar, Sehoon Ha, Gergory Sawicki, C. Karen Liu
分類: cs.RO
原文アブストラクト
Fall prevention is one of the most important components in senior care. We present a technique to augment an assistive walking device with the ability to prevent falls. Given an existing walking device, our method develops a fall predictor and a recovery policy by utilizing the onboard sensors and actuators. The key component of our method is a robust human walking policy that models realistic human gait under a moderate level of perturbations. We use this human walking policy to provide training data for the fall predictor, as well as to teach the recovery policy how to best modify the person's gait when a fall is imminent. Our evaluation shows that the human walking policy generates walking sequences similar to those reported in biomechanics literature. Our experiments in simulation show that the augmented assistive device can indeed help recover balance from a variety of external perturbations. We also provide a quantitative method to evaluate the design choices for an assistive device.
