Tactile sensing on human feet is crucial for motion control, however, has not been explored in robotic counterparts. This work is dedicated to endowing tactile sensing to legged robot's feet and showing that a single-legged robot can be stabilized with only tactile sensing signals from its foot. We propose a robot leg with a novel vision-based tactile sensing foot system and implement a processing algorithm to extract contact information for feedback control in stabilizing tasks. A pipeline to convert images of the foot skin into high-level contact information using a deep learning framework is presented. The leg was quantitatively evaluated in a stabilization task on a tilting surface to show that the tactile foot was able to estimate both the surface tilting angle and the foot poses. Feasibility and effectiveness of the tactile system were investigated qualitatively in comparison with conventional single-legged robotic systems using inertia measurement units (IMU). Experiments demonstrate the capability of vision-based tactile sensors in assisting legged robots to maintain stability on unknown terrains and the potential for regulating more complex motions for humanoid robots.
翻译:然而,在机器人对等器中尚未探索对运动控制至关重要的人体脚上的触摸感知,但该工作致力于将触摸感测到机械人的脚部,并表明单脚机器人只能用触摸感应信号稳定下来。我们建议使用新型的视觉触摸感知脚系统,采用一种处理算法来提取在稳定任务中进行反馈控制的接触信息。介绍了利用深层学习框架将脚皮图像转换成高层接触信息的管道。在倾斜表面的稳定化任务中,对腿进行了定量评估,以显示触摸脚能够估计表面倾斜角度和脚部。对触摸系统的可行性和有效性进行了定性调查,与使用惯性测量器(IMU)的常规单腿机械系统相比。实验表明基于视觉的触摸感感在协助脚机器人维持未知地形的稳定以及调节人类机器人更复杂运动的潜力方面的能力。