Toward Artificial Palpation: Representation Learning of Touch on Soft Bodies
This work addresses the challenge of replicating human touch in medical examinations, offering a proof-of-concept for artificial palpation, though it is incremental as it builds on existing encoder-decoder frameworks.
The paper tackles the problem of automating medical palpation by proposing a self-supervised learning method that learns a representation from tactile measurements, which is validated on simulated and real-world datasets for tasks like tactile imaging and change detection.
Palpation, the use of touch in medical examination, is almost exclusively performed by humans. We investigate a proof of concept for an artificial palpation method based on self-supervised learning. Our key idea is that an encoder-decoder framework can learn a $\textit{representation}$ from a sequence of tactile measurements that contains all the relevant information about the palpated object. We conjecture that such a representation can be used for downstream tasks such as tactile imaging and change detection. With enough training data, it should capture intricate patterns in the tactile measurements that go beyond a simple map of forces -- the current state of the art. To validate our approach, we both develop a simulation environment and collect a real-world dataset of soft objects and corresponding ground truth images obtained by magnetic resonance imaging (MRI). We collect palpation sequences using a robot equipped with a tactile sensor, and train a model that predicts sensory readings at different positions on the object. We investigate the representation learned in this process, and demonstrate its use in imaging and change detection.