Toward Force Estimation in Robot-Assisted Surgery using Deep Learning with Vision and Robot State

Knowledge of interaction forces during teleoperated robot-assisted surgery could be used to enable force feedback to users and evaluate tissue handling skill. However, direct force sensing at the end-effector is challenging because it requires biocompatible, sterilizable, and cost-effective sensors. Vision-based neural networks are a promising approach for providing useful force estimates, though questions remain about generalization to new scenarios and real-time inference. We present a force estimation neural network that uses RGB images and robot state as inputs. Using a self-collected dataset, we compared the network to variants that included only a single input type, and evaluated how they generalized to new viewpoints, workspace positions, materials, and tools. We found that the vision-only network was sensitive to shifts in viewpoints, while networks with state inputs were sensitive to vertical shifts in workspace. The network with both state and vision inputs had the highest accuracy for an unseen tool, while the state-only network was most accurate for an unseen material. Through feature removal studies, we found that using only force features produced better accuracy than using only kinematic features as input. The network with both state and vision inputs outperformed a physics-based model in accuracy for seen material. It showed comparable accuracy but faster computation times than a recurrent neural network, making it better suited for real-time applications.