Walking robots are considered as a promising solution for locomotion across irregular or rough terrain. While wheeled or tracked robots require flat surface like roads or driveways, walking robots can adapt to almost any terrain type. However, overcoming diverse terrain obstacles still remains a challenging task even for multi-legged robots with a high number of degrees of freedom. Here, we present a novel method for obstacle overcoming for walking robots based on the use of tactile sensors and generative recurrent neural network for positional error prediction. By using tactile sensors positioned on the front side of the legs, we demonstrate that a robot is able to successfully overcome obstacles close to robots height in the terrains of different complexity. The proposed method can be used by any type of a legged machine and can be considered as a step toward more advanced walking robot locomotion in unstructured terrain and uncertain environment.

步行机器人被认为是在不规则或崎terrain地形上运动的有前途的解决方案。轮式或履带式机器人需要平坦的路面，例如道路或车道，而步行机器人几乎可以适应任何地形类型。但是，即使对于具有多个自由度的多足机器人，克服各种地形障碍仍然是一项艰巨的任务。在这里，我们提出一种基于触觉传感器和生成式递归神经网络的位置误差预测的行走机器人克服障碍的新方法。通过使用位于腿部前侧的触觉传感器，我们证明了机器人能够成功克服各种复杂地形中接近机器人高度的障碍。