A surgical system for automatic registration, stiffness mapping and dynamic image overlay

In this paper we develop a surgical system using the da Vinci research kit (dVRK) that is capable of autonomously searching for tumors and dynamically displaying the tumor location using augmented reality. Such a system has the potential to quickly reveal the location and shape of tumors and visually overlay that information to reduce the cognitive overload of the surgeon. We believe that our approach is one of the first to incorporate state-of-the-art methods in registration, force sensing and tumor localization into a unified surgical system. First, the preoperative model is registered to the intra-operative scene using a Bingham distribution-based filtering approach. An active level set estimation is then used to find the location and the shape of the tumors. We use a recently developed miniature force sensor to perform the palpation. The estimated stiffness map is then dynamically overlaid onto the registered preoperative model of the organ. We demonstrate the efficacy of our system by performing experiments on phantom prostate models with embedded stiff inclusions.Comment: International Symposium on Medical Robotics (ISMR 2018

Active Clothing Material Perception using Tactile Sensing and Deep Learning

Humans represent and discriminate the objects in the same category using their properties, and an intelligent robot should be able to do the same. In this paper, we build a robot system that can autonomously perceive the object properties through touch. We work on the common object category of clothing. The robot moves under the guidance of an external Kinect sensor, and squeezes the clothes with a GelSight tactile sensor, then it recognizes the 11 properties of the clothing according to the tactile data. Those properties include the physical properties, like thickness, fuzziness, softness and durability, and semantic properties, like wearing season and preferred washing methods. We collect a dataset of 153 varied pieces of clothes, and conduct 6616 robot exploring iterations on them. To extract the useful information from the high-dimensional sensory output, we applied Convolutional Neural Networks (CNN) on the tactile data for recognizing the clothing properties, and on the Kinect depth images for selecting exploration locations. Experiments show that using the trained neural networks, the robot can autonomously explore the unknown clothes and learn their properties. This work proposes a new framework for active tactile perception system with vision-touch system, and has potential to enable robots to help humans with varied clothing related housework.Comment: ICRA 2018 accepte

To “Sketch-a-Scratch”

A surface can be harsh and raspy, or smooth and silky, and everything in between. We are used to sense these features with our fingertips as well as with our eyes and ears: the exploration of a surface is a multisensory experience. Tools, too, are often employed in the interaction with surfaces, since they augment our manipulation capabilities. “Sketch-a-Scratch” is a tool for the multisensory exploration and sketching of surface textures. The user’s actions drive a physical sound model of real materials’ response to interactions such as scraping, rubbing or rolling. Moreover, different input signals can be converted into 2D visual surface profiles, thus enabling to experience them visually, aurally and haptically

Bodily awareness and novel multisensory features

According to the decomposition thesis, perceptual experiences resolve without remainder into their different modality-specific components. Contrary to this view, I argue that certain cases of multisensory integration give rise to experiences representing features of a novel type. Through the coordinated use of bodily awareness—understood here as encompassing both proprioception and kinaesthesis—and the exteroceptive sensory modalities, one becomes perceptually responsive to spatial features whose instances couldn’t be represented by any of the contributing modalities functioning in isolation. I develop an argument for this conclusion focusing on two cases: 3D shape perception in haptic touch and experiencing an object’s egocentric location in crossmodally accessible, environmental space

Site Interiography and Geophysical Scanning: Interpreting the Texture and Form of Archaeological Deposits with Ground-Penetrating Radar

The remarkable potential of geophysical scanning—to assess the internal variability of sites in new ways, to highlight important phenomena in the field, to exercise co-creation of interpretation and commitment to minimal destruction of community partners’ resources, and to aid in the practice of due diligence in avoiding desecration of the sacred—continues to be underutilized in archaeology. While archaeological artifacts, features, and strata remain primary foci of archaeological geophysics, these phenomena are perceived quite differently in scans than in visual or tactile exposures. In turn, new registers of site exploration afforded by geophysical prospection may be constrained by the language of site excavation and visual observation, requiring adjustments in the ways of thinking about and describing what the instruments are measuring. The texture and form of site deposits as rendered in ground-penetrating radar scans can be examined in detail prior to making interpretations of cultural features or stratigraphy. Far more than simple “anomalies” demanding our attention for excavation, patterns in geophysical data can be the focus of extensive archaeological analysis prior to, in conjunction with, or independent from excavation