  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.
