论文标题
首先不要跌倒：学会用损坏的人形机器人利用墙壁
First do not fall: learning to exploit a wall with a damaged humanoid robot
论文作者
论文摘要
人形机器人可以在危险情况下取代人类，但大多数这种情况对他们来说同样危险，这意味着他们有很大的损害和跌倒的机会。我们假设人形机器人将主要用于建筑物，这使它们可能靠近墙壁。为了避免跌倒，他们可以像人类那样靠在最接近的墙上，只要他们在几毫秒内找到手放手的地方。本文介绍了一种称为D-Reflex的方法，该方法学习了一个神经网络，该神经网络在墙壁方向，墙距离和机器人的姿势下选择此接触位置。然后，全身控制器使用此接触位置来达到稳定的姿势。我们表明，D-Reflex允许模拟的Talos机器人（17.5m，100kg，30自由度）避免了超过75％的可避免跌倒，并且可以在真正的机器人上工作。
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.
