Learning Based Estimation of Tool-Tissue Interaction Forces for Stationary and Moving Environments

Accurately estimating tool-tissue interaction forces during robotics-assisted minimally invasive surgery is an important aspect of enabling haptics-based teleoperation. By collecting data regarding the state of a robot in a variety of configurations, neural networks can be trained to predict this interaction force. This paper extends existing work in this domain based on collecting one of the largest known ground truth force datasets for stationary as well as moving phantoms that replicate tissue motions found in clinical procedures. Existing methods, and a new transformer-based architecture, are evaluated to demonstrate the domain gap between stationary and moving phantom tissue data and the impact that data scaling has on each architecture's ability to generalize the force estimation task. It was found that temporal networks were more sensitive to the moving domain than single-sample Feed Forward Networks (FFNs) that were trained on stationary tissue data. However, the transformer approach results in the lowest Root Mean Square Error (RMSE) when evaluating networks trained on examples of both stationary and moving phantom tissue samples. The results demonstrate the domain gap between stationary and moving surgical environments and the effectiveness of scaling datasets for increased accuracy of interaction force prediction.