Intelligent Robotic Sonographer: Mutual Information-based Disentangled Reward Learning from Few Demonstrations

Introduction

This work proposes an intelligent robotic sonographer to autonomously “explore” target anatomies and navigate a US probe to standard planes by learning from the expert. The underlying high-level physiological knowledge from experts is inferred by a neural reward function, using a ranked pairwise image comparisons approach in a self-supervised fashion. This process can be referred to as understanding the “language of sonography”.

Considering the generalization capability to overcome inter-patient variations, mutual information is estimated by a network to explicitly disentangle the task-related and domain features in latent space.

Video Demo

Citation

If you found this work interesting and adopted part of it to your own research, or if this work inspires your research, you can cite our paper by:

@article{jiang2023intelligent,
  title={Intelligent robotic sonographer: Mutual information-based disentangled reward learning from few demonstrations},
  author={Jiang, Zhongliang and
          Bi, Yuan and
          Zhou, Mingchuan and
          Hu, Ying and
          Burke, Michael and
          Navab, Nassir},
  journal={The International Journal of Robotics Research},
  pages={02783649231223547},
  publisher={SAGE Publications Sage UK: London, England}
}