Inertial Measurement Unit (IMU) is ubiquitous in robotic research. It provides posture information for robots to realize balance and navigation. However, humans and animals can perceive the movement of their bodies in the environment without precise orientation or position values. This interaction inherently involves a fast feedback loop between perception and action. This work proposed an end-to-end approach that uses high dimension visual observation and action commands to train a visual self-model for legged locomotion. The visual self-model learns the spatial relationship between the robot body movement and the ground texture changes from image sequences. We demonstrate that the robot can leverage the visual self-model to achieve various locomotion tasks in the real-world environment that the robot does not see during training. With our proposed method, robots can do locomotion without IMU or in an environment with no GPS or weak geomagnetic fields like the indoor and urban canyons in the city.

翻译：惯性测量单元在机器人研究中应用广泛，可为机器人提供姿态信息以实现平衡与导航。然而，人类和动物能在缺乏精确朝向或位置数值的情况下感知自身在环境中的运动，这种交互本质上涉及感知与动作之间的快速反馈回路。本文提出一种端到端方法，利用高维视觉观测与动作指令训练足式运动的视觉自模型。该模型从图像序列中学习机器人身体运动与地面纹理变化之间的空间关联。实验表明，机器人可借助该视觉自模型在训练中未见的真实环境中完成多种运动任务。采用本方法后，机器人可在无惯性测量单元、无全球定位系统或地磁场较弱的场景（如城市室内及楼宇峡谷）中实现运动控制。