In this work, we propose a semi-autonomous scheme to synergistically share the complicated task of manipulation and cutting of an unknown deformable tissue (U-DT) between a remote surgeon and a surgical robot. Particularly, utilizing the da Vinci Research Kit (dVRK) platform, we have designed and successfully demonstrated a fully functional shared control scheme for an autonomous tensioning and tele-cutting of a U-DT. We have shown the system’s ability to cooperate with a remote surgeon by leveraging an online data-driven learning and adaptive control method coupled with a reduced-order trajectory planning module that depends on just two parameters. By performing 25 experiments on custom-designed silicon phantoms and defining a set of success/failure metrics, we have put forward findings that establish a causal relationship between these two important parameters and the success or failure of the performed experiments.

Communication latency in any delicate telerobotic operation (such as remote surgery over distance) would impose a significant challenge due to the temporal degradation of visual perception and can substantially affect the outcomes. Less is known, however, about the neurophysiological basis of how operators adapt/react to delayed visual feedback. Identification of such neural markers might provide novel ways for future applications to monitor the mental workload (MW). In this study, we recorded electroencephalography (EEG) data from nine users while performing a peg transfer task using the da Vinci Research Kit with three levels of induced visual delay in the video feedback. Our results suggest that spectral EEG-based features can provide markers of the operator's MW modulated by arbitrary visual delay. We also show that the exposure to different visual delays could be successfully classified/detected solely from EEG data, using a Riemannian geometry-based classifier, which highlights the utility of EEG signals for detecting the effect of visual delay on brain activity.