  This paper introduces the Terrain Recognition And Contact Force Estimation
Paw, a compact and sensorized shoe designed for legged robots. The paw
end-effector is made of silicon that deforms upon the application of contact
forces, while an embedded micro camera is utilized to capture images of the
deformed inner surface inside the shoe, and a microphone picks up audio
signals. Processed through machine learning techniques, the images are mapped
to compute an accurate estimate of the cumulative 3D force vector, while the
audio signals are analyzed to identify the terrain class (e.g., gravel, snow).
By leveraging its on-edge computation ability, the paw enhances the
capabilities of legged robots by providing key information in real-time that
can be used to adapt locomotion control strategies. To assess the performance
of this novel sensorized paw, we conducted experiments on the data collected
through a specially-designed testbed for force estimation, as well as data from
recordings of the audio signatures of different terrains interacting with the
paw. The results demonstrate the accuracy and effectiveness of the system,
highlighting its potential for improving legged robot performance.
