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Background Surgical robots are becoming more common in the operating room. Although surgeons utilize these robots for improved dexterity, scalable movements, and enhanced vision, they lose their sense of tactile and haptic feedback [1]. Okamura demonstrated a negative consequence of this by showing that forces exerted during robotic sutures significantly exceed that of hand sutures [2]. This excess in force can lead to a variety of complications including tissue crushing, which has been shown to be a clinically relevant problem [3]. Sie et al. proposed tissue-aware grasping as a solution for tissue crush injury, which may obviate the need for tactile and haptic feedback altogether [4]. By coupling online tissue identification with tissue-specific thresholds for crush injury, the surgical robot can warn of imminent tissue crushing or potentially prevent it. Sie et al. provided relevant work in this area by validating an approach for online tissue identification within the first 0.3 s of a grasp. This work was done with a modified manual laparoscopic Babcock grasper; this is a specialized instrument not commonly used in surgery. We herein aim to extend the results of Sie et al. to a much more common surgical tool: the da Vinci EndoWrist surgical instrument (Intuitive Surgical, Sunnyvale, CA). We demonstrate that tissue identification is possible using existing robotic tools without additional sensors or modifications to the tool tip, by using only motor torque and position data at the proximal end of the tool.

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