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Estimation of Interaction Forces in Robotic Surgery using a Semi-Supervised Deep Neural Network Model

 
: Marban, A.; Srinivasan, V.; Samek, W.; Fernandez, J.; Casals, A.

:

Institute of Electrical and Electronics Engineers -IEEE-:
IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018 : 1-5 October 2018, Madrid, Spain
Piscataway, NJ: IEEE, 2018
ISBN: 978-1-5386-8094-0
ISBN: 978-1-5386-8093-3
ISBN: 978-1-5386-8095-7
S.761-768
International Conference on Intelligent Robots and Systems (IROS) <2018, Madrid>
Englisch
Konferenzbeitrag
Fraunhofer HHI ()

Abstract
Providing force feedback as a feature in current Robot-Assisted Minimally Invasive Surgery systems still remains a challenge. In recent years, Vision-Based Force Sensing (VBFS) has emerged as a promising approach to address this problem. Existing methods have been developed in a Supervised Learning (SL) setting. Nonetheless, most of the video sequences related to robotic surgery are not provided with ground-truth force data, which can be easily acquired in a controlled environment. A powerful approach to process unlabeled video sequences and find a compact representation for each video frame relies on using an Unsupervised Learning (UL) method. Afterward, a model trained in an SL setting can take advantage of the available ground-truth force data. In the present work, UL and SL techniques are used to investigate a model in a Semi-Supervised Learning (SSL) framework, consisting of an encoder network and a Long-Short Term Memory (LSTM) network. First, a Convolutional Auto-Encoder (CAE) is trained to learn a compact representation for each RGB frame in a video sequence. To facilitate the reconstruction of high and low frequencies found in images, this CAE is optimized using an adversarial framework and a L1-loss, respectively. Thereafter, the encoder network of the CAE is serially connected with an LSTM network and trained jointly to minimize the difference between ground-truth and estimated force data. Datasets addressing the force estimation task are scarce. Therefore, the experiments have been validated in a custom dataset. The results suggest that the proposed approach is promising.

: http://publica.fraunhofer.de/dokumente/N-581399.html