  This paper presents the first implementation of autonomous robotic
auscultation of heart and lung sounds. To select auscultation locations that
generate high-quality sounds, a Bayesian Optimization (BO) formulation
leverages visual anatomical cues to predict where high-quality sounds might be
located, while using auditory feedback to adapt to patient-specific anatomical
qualities. Sound quality is estimated online using machine learning models
trained on a database of heart and lung stethoscope recordings. Experiments on
4 human subjects show that our system autonomously captures heart and lung
sounds of similar quality compared to tele-operation by a human trained in
clinical auscultation. Surprisingly, one of the subjects exhibited a previously
unknown cardiac pathology that was first identified using our robot, which
demonstrates the potential utility of autonomous robotic auscultation for
health screening.
