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1. Collaborative Robotics Toolkit (CRTK): Open Software Framework for Surgical Robotics Research

   Su, Yun-Hsuan ; Munawar, Adnan ; Deguet, Anton ; Lewis, Andrew ; Lindgren, Kyle ; Li, Yangming ; Taylor, Russell H. ; Fischer, Gregory S. ; Hannaford, Blake ; Kazanzides, Peter ( November 2020 , IEEE International Conference on Robotic Computing (IRC))

   null (Ed.)

   Robot-assisted minimally invasive surgery has made a substantial impact in operating rooms over the past few decades with their high dexterity, small tool size, and impact on adoption of minimally invasive techniques. In recent years, intelligence and different levels of surgical robot autonomy have emerged thanks to the medical robotics endeavors at numerous academic institutions and leading surgical robot companies. To accelerate interaction within the research community and prevent repeated development, we propose the Collaborative Robotics Toolkit (CRTK), a common API for the RAVEN-II and da Vinci Research Kit (dVRK) - two open surgical robot platforms installed at more than 40 institutions worldwide. CRTK has broadened to include other robots and devices, including simulated robotic systems and industrial robots. This common API is a community software infrastructure for research and education in cutting edge human-robot collaborative areas such as semi-autonomous teleoperation and medical robotics. This paper presents the concepts, design details and the integration of CRTK with physical robot systems and simulation platforms. 

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2. From the Dexterous Surgical Skill to the Battlefield—A Robotics Exploratory Study

   https://doi.org/10.1093/milmed/usaa253  

   Gonzalez, Glebys T ; Kaur, Upinder ; Rahman, Masudur ; Venkatesh, Vishnunandan ; Sanchez, Natalia ; Hager, Gregory ; Xue, Yexiang ; Voyles, Richard ; Wachs, Juan ( January 2021 , Military Medicine)

   null (Ed.)

   ABSTRACT Introduction Short response time is critical for future military medical operations in austere settings or remote areas. Such effective patient care at the point of injury can greatly benefit from the integration of semi-autonomous robotic systems. To achieve autonomy, robots would require massive libraries of maneuvers collected with the goal of training machine learning algorithms. Although this is attainable in controlled settings, obtaining surgical data in austere settings can be difficult. Hence, in this article, we present the Dexterous Surgical Skill (DESK) database for knowledge transfer between robots. The peg transfer task was selected as it is one of the six main tasks of laparoscopic training. In addition, we provide a machine learning framework to evaluate novel transfer learning methodologies on this database. Methods A set of surgical gestures was collected for a peg transfer task, composed of seven atomic maneuvers referred to as surgemes. The collected Dexterous Surgical Skill dataset comprises a set of surgical robotic skills using the four robotic platforms: Taurus II, simulated Taurus II, YuMi, and the da Vinci Research Kit. Then, we explored two different learning scenarios: no-transfer and domain-transfer. In the no-transfer scenario, the training and testing data were obtained from the same domain; whereas in the domain-transfer scenario, the training data are a blend of simulated and real robot data, which are tested on a real robot. Results Using simulation data to train the learning algorithms enhances the performance on the real robot where limited or no real data are available. The transfer model showed an accuracy of 81% for the YuMi robot when the ratio of real-tosimulated data were 22% to 78%. For the Taurus II and the da Vinci, the model showed an accuracy of 97.5% and 93%, respectively, training only with simulation data. Conclusions The results indicate that simulation can be used to augment training data to enhance the performance of learned models in real scenarios. This shows potential for the future use of surgical data from the operating room in deployable surgical robots in remote areas. 

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3. A Perspective on Miniature Soft Robotics: Actuation, Fabrication, Control, and Applications

   https://doi.org/10.1002/aisy.202300063  

   Chi, Yinding ; Zhao, Yao ; Hong, Yaoye ; Li, Yanbin ; Yin, Jie ( April 2023 , Advanced Intelligent Systems)

   Soft robotics enriches the robotic functionalities by engineering soft materials and electronics toward enhanced compliance, adaptivity, and friendly human machine. This decade has witnessed extraordinary progresses and benefits in scaling down soft robotics to small scale for a wide range of potential and promising applications, including medical and surgical soft robots, wearable and rehabilitation robots, and unconstructed environments exploration. This perspective highlights recent research efforts in miniature soft robotics in a brief and comprehensive way in terms of actuation, powering, designs, fabrication, control, and applications in four sections. Section 2 discusses the key aspects of materials selection and structural designs for small‐scale tethered and untethered actuation and powering, including fluidic actuation, stimuli‐responsive actuation, and soft living biohybrid materials, as well as structural forms from 1D to 3D. Section 3 discusses the advanced manufacturing techniques at small scales for fabricating miniature soft robots, including lithography, mechanical self‐assembly, additive manufacturing, tissue engineering, and other fabrication methods. Section 4 discusses the control systems used in miniature robots, including off‐board/onboard controls and artificial intelligence‐based controls. Section 5 discusses their potential broad applications in healthcare, small‐scale objects manipulating and processing, and environmental monitoring. Finally, outlooks on the challenges and opportunities are discussed.

    

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4. Managing social-educational robotics for students with autism spectrum disorder through business model canvas and customer discovery

   https://doi.org/10.3389/frobt.2024.1328467  

   Arora, Anshu Saxena ; Arora, Amit ; Sivakumar, K ; McIntyre, John R ( April 2024 , Frontiers in Robotics and AI)

   Social-educational robotics, such as NAO humanoid robots with social, anthropomorphic, humanlike features, are tools for learning, education, and addressing developmental disorders (e.g., autism spectrum disorder or ASD) through social and collaborative robotic interactions and interventions. There are significant gaps at the intersection of social robotics and autism research dealing with how robotic technology helps ASD individuals with their social, emotional, and communication needs, and supports teachers who engage with ASD students. This research aims to (a) obtain new scientific knowledge on social-educational robotics by exploring the usage of social robots (especially humanoids) and robotic interventions with ASD students at high schools through an ASD student–teacher co-working with social robot–social robotic interactions triad framework; (b) utilize Business Model Canvas (BMC) methodology for robot design and curriculum development targeted at ASD students; and (c) connect interdisciplinary areas of consumer behavior research, social robotics, and human-robot interaction using customer discovery interviews for bridging the gap between academic research on social robotics on the one hand, and industry development and customers on the other. The customer discovery process in this research results in eight core research propositions delineating the contexts that enable a higher quality learning environment corresponding with ASD students’ learning requirements through the use of social robots and preparing them for future learning and workforce environments.

    

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5. Real-Time Context-Aware Detection of Unsafe Events in Robot-Assisted Surgery

   https://doi.org/10.1109/DSN48063.2020.00054  

   Yasar, Mohammad Samin ; Alemzadeh, Homa ( June 2020 , 2020 50th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN))

   Cyber-physical systems for robotic surgery have enabled minimally invasive procedures with increased precision and shorter hospitalization. However, with increasing complexity and connectivity of software and major involvement of human operators in the supervision of surgical robots, there remain significant challenges in ensuring patient safety. This paper presents a safety monitoring system that, given the knowledge of the surgical task being performed by the surgeon, can detect safety-critical events in real-time. Our approach integrates a surgical gesture classifier that infers the operational context from the time-series kinematics data of the robot with a library of erroneous gesture classifiers that given a surgical gesture can detect unsafe events. Our experiments using data from two surgical platforms show that the proposed system can detect unsafe events caused by accidental or malicious faults within an average reaction time window of 1,693 milliseconds and F1 score of 0.88 and human errors within an average reaction time window of 57 milliseconds and F1 score of 0.76. 

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