  Haptic perception is highly important for immersive teleoperation of robots,
especially for accomplishing manipulation tasks. We propose a low-cost haptic
sensing and rendering system, which is capable of detecting and displaying
surface roughness. As the robot fingertip moves across a surface of interest,
two microphones capture sound coupled directly through the fingertip and
through the air, respectively. A learning-based detector system analyzes the
data in real time and gives roughness estimates with both high temporal
resolution and low latency. Finally, an audio-based vibrational actuator
displays the result to the human operator. We demonstrate the effectiveness of
our system through lab experiments and our winning entry in the ANA Avatar
XPRIZE competition finals, where briefly trained judges solved a
roughness-based selection task even without additional vision feedback. We
publish our dataset used for training and evaluation together with our trained
models to enable reproducibility of results.
