Robot Programming Research Articles

HPB SO08 - Safety of Robotic cholecystectomy as index training procedure – the UK experience

Abstract Background A recent large retrospective cohort study reported a higher risk of bile duct injury associated with robotic cholecystectomy when compared to the laparoscopic approach. Considering that most robotic training pathways, including the one followed in the UK, utilise robotic cholecystectomy as index training procedure, such findings mandate evaluation of the safety profile of robotic cholecystectomy in the UK to ensure its use as an alternative approach to laparoscopic cholecystectomy is not putting patients at risk. We aimed to conduct a multi-centre observational study to evaluate the safety profile of robotic cholecystectomy performed within the UK HPB Robotic training program. Method A retrospective analysis of prospectively collected data from eleven centres participating in the UK robotic HPB training program was conducted. All adult patients undergoing robotic cholecystectomy for symptomatic gallstone disease or gallbladder polyp were considered. Bile duct injury, conversion to open procedure, conversion to subtotal cholecystectomy, length of hospital stay, 30-day re-admission, and post-operative complications were the evaluated outcome parameters. Results A total of 600 patients were included. The median age was 53 (IQR 65 - 41) years and the majority (72.7%; 436/600) were female. The main indications for robotic cholecystectomy were biliary colic (55.5%, 333/600), cholecystitis (18.8%, 113/600), gallbladder polyps (7.7%, 46/600) and pancreatitis (6.2%, 37/600). The median length of stay was 0 (IQR: 0-1) days. Of the included patients, 88.5% (531/600) were discharged on the day of procedure with 30-day re-admission rate of 5.5% (33/600). There were no bile duct injuries and the rate of conversion to open was 0.8% (5/600) with subtotal cholecystectomy rate of 0.8% (5/600). Conclusion This current study confirms that robotic cholecystectomy can be safely implemented to routine practice with a low risk of bile duct injury, low conversion to open surgery and low need for subtotal cholecystectomy.

OGC SO45 - Effect of anastomotic technique on leak rates in two-stage robotic-assisted subtotal oesophagectomy

Abstract Background Introduction of new surgical techniques requires constant monitoring and real time audit. In our unit the introduction of the thoracic component of our robotic oesophagectomy program resulted in an increase in the anastomotic leak rate which was recognised in real time. This prompted a decision to change from a circular to a linear stapled technique. The aim of this study was to evaluate the impact of this change in technique on the anastomotic leak rate. Method A database was used to identify all cases of patients undergoing two stage subtotal oesophagectomy where the thoracic phase was performed robotically-assisted. Demographic, operative, pathological and complications data were extracted. An anastomotic leak was defined as radiological or endoscopic disruption of the oesophagogastric anastomosis or where a clinician had documented the presence of a leak in the clinical notes. Leaks were then graded by severity according to the classification by Low et al into type 1: localised defect treated with medical therapy or observation; Type 2: localised defect requiring intervention but not surgery, and; Type 3: localised defects requiring surgery. Results Fifty-seven patients were identified. The first 25 cases were reconstructed with a circular-stapled and the subsequent 32 with a linear-stapled anastomosis. There were no differences in baseline demographics. There were no differences in thoracic operating time nor blood loss, intraoperative complications and pathological variables such as lymph node yield. Overall, 11/57 patients (19%) had an anastomotic leak, of which 7 (28%) had a circular stapled anastomosis and 4 (12%) had a linear stapled anastomosis. This difference was not statistically significant (p=0.2384). However, CUSUM analysis after the change in anastomotic technique showed an ongoing and diminished anastomotic leak rate. Conclusion In our unit changing from a circular to a linear stapled anastomotic technique resulted in a reduction in the anastomotic leak rate from 24% to 12%. While this was not statistically significant, this is a clinically appreciable result and this benefit was confirmed on CUSUM analysis.

OGC SO16 - International upper GI robotic fellowship on the CMR Versius platform

Abstract Background The impetus for UGI surgery trainees to attain skills in robotic surgery is likely to increase following the global acceptance and utilisation of robotic platforms. UGI robotic fellowship programs are few in number and highly desirable providing trainees with a unique selling point when embarking on independent consultant practice. We describe the first UGI robotic surgery fellowship on the CMR Versius platform at the Gloucestershire Royal Hospital which has the largest experience of UGI robotic surgery on the CMR Versius platform in the UK, having performed over 200 cases including the first robot assisted minimally invasive oesophagectomy. Method A structured robotic training curriculum was created in collaboration with CMR Versius. It consists of a pre-clinical phase of E-learning (10 modules), simulator training (16 assessed tasks) and dry-laboratory training (a two-day course encompassing robotic system set-up, bed-side assist and assessed dry lab practical tasks). The clinical phase begins with bed-side assisting of robotic cases followed by supervised operating on the console. Operations performed on the console are recorded for review and evaluation. The videos will be scored by two independent surgeons using the modified Global Evaluation Assessment of Robotic Skills (mGEARS) score to track growth and progress. Results The E-learning modules and assessments were completed prior to commencement of the fellowship. The remainder of the pre-clinical phase of the curriculum were completed within 4 weeks of commencement followed by bed-side assisting. Once competent at bed-side assisting and able to demonstrate proficiency in set-up (selecting port site placement, docking, arm spacing and configuration), supervised console training was commenced 6 weeks into the fellowship. Console training began with robotic assisted laparoscopic assisted cholecystectomy to gain familiarity, confidence, and efficiency. After 6 cases as the only console surgeon, the surgical complexity of cases was progressed to hiatal surgery and Heller’s cardiomyotomy Conclusion A structured training curriculum with defined phases provides a feasible and effective framework for robotic fellowship training. Adherence to the pre-clinical phase of training allows safe and efficient progression to hands-on console experience. This is achievable within relatively short timeframe to allow for ample independent console surgeon experience and the development of proficiency within a 12-month fellowship period.

Agreeing to Disagree: Translating Representations to Uncover a Unified Representation for Social Robot Actions

Researchers and designers of social robots often approach robot control system design from a single perspective; such as designing autonomous robots, teleoperated robots, or robots programmed by an end-user. While each design approach presents a tradeoff between some advantages and limitations, there is an opportunity to integrate these approaches where people benefit from the best-fit approach for their use case. In this work, we propose integrating these seemingly distinct robot control approaches to uncover a common data representation of social actions defining social expression by a robot. We demonstrate the value of integrating an authoring system, teleoperation interface, and robot planning system by integrating instances of these systems for robot storytelling. By relying on an integrated system, domain experts can define behaviors through end-user interfaces that teleoperators and autonomous robot programmers can use directly thus providing a cohesive expert-driven robot system.

Assessment of first-touch skills in robotic surgical training using hi-Sim and the hinotori surgical robot system among surgeons and novices

PurposeSurgeons’ adaptability to robotic manipulation remains underexplored. This study evaluated the participants’ first-touch robotic training skills using the hinotori surgical robot system and its simulator (hi-Sim) to assess adaptability.MethodsWe enrolled 11 robotic surgeons (RS), 13 laparoscopic surgeons (LS), and 15 novices (N). After tutorial and training, participants performed pegboard tasks, camera and clutch operations, energizing operations, and suture sponge tasks on hi-Sim. They also completed a suture ligation task using the hinotori surgical robot system on a suture simulator. Median scores and task completion times were compared.ResultsPegboard task scores were 95.0%, 92.0%, and 91.5% for the RS, LS, and N groups, respectively, with differences between the RS group and LS and N groups. Camera and clutch operation scores were 93.1%, 49.7%, and 89.1%, respectively, showing differences between the RS group and LS and N groups. Energizing operation scores were 90.9%, 85.2%, and 95.0%, respectively, with a significant difference between the LS and N groups. Suture sponge task scores were 90.6%, 43.1%, and 46.2%, respectively, with differences between the RS group and LS and N groups. For the suture ligation task, completion times were 368 s, 666 s, and 1095 s, respectively, indicating differences among groups. Suture scores were 12, 10, and 7 points, respectively, with differences between the RS and N groups.ConclusionFirst-touch simulator-based robotic skills were partially influenced by prior robotic surgical experience, while suturing skills were affected by overall surgical experience. Thus, robotic training programs should be tailored to individual adaptability.

Creating Digital Twins of Robotic Stations Using a Laser Tracker

This article deals with the design and creation of digital twins of robotic stations. A literature review of digital twins, robot programming methods and laser tracker applications is presented. This paper shows that the construction of digital twins is closely related to one of the most popular methods of robot programming, i.e., off-line programming. In the case of digital twins of robotic stations, modeling accuracy and two-way communication with the real station proved to be crucial. The article proposes a methodology for solving the basic problem of off-line robot programming, i.e., the limited accuracy of the representation of the station and the details. The algorithm of proceeding in the case when the station already exists and its digital model is built and the case when the digital model is first created and the real solution is built on its basis is shown. According to the developed methodology, a digital twin of a real robotic station was created and the possibilities arising from the use of virtual tools were shown. The developed digital twin has the ability to communicate with advanced Matlab 2021-type tools, uses cloud solutions and virtual and augmented reality for training, simulates physical phenomena and provides the ability to accurately program robots off-line.

Arduino-Based Mobile Robotics for Fostering Computational Thinking Development: An Empirical Study with Elementary School Students Using Problem-Based Learning Across Europe

The present article explores the impact of educational robotics on fostering computational thinking and problem-solving skills in elementary school students through a problem-based learning approach. This study involved the creation of a framework which includes a robot and two eBooks designed for students and teachers. The eBooks serve as a guide to the construction and programming of a small Arduino-based robot. Through integration with gamification elements, the model features a narrative with three characters to boost a student’s engagement and motivation. Through iteration of heuristic evaluations and practical tests, we refined the initial theoretical framework. An empirical study was conducted in two phases involving 350 students. The first empirical test involved a small group of 21 students, similar to end users, from five European schools. With a 100% completion rate for the tasks, 73.47% of these tasks were solved optimally. Later, we conducted a larger validation study which involved 329 students in a Portuguese school. This second phase of the study was conducted during the 2022–2023 and 2023–2024 school years with three study groups. The results led to a 91.13% success rate in problem-solving activities, and 56.99% of those students achieved optimal solutions. Advanced statistical techniques, including ANOVA, were applied to account for group differences and ensure the robustness of the findings. This study demonstrates that the proposed model which integrates educational robotics with problem-based learning effectively promotes computational thinking and problem-solving skills, which are essential for the 21st century. These findings support the inclusion of robotics into primary school curricula and provide a validated framework for educators.

Advancing STEM Learning Opportunities for Students with Autism Spectrum Disorder Through an Informal Robotics and Coding Program: A Feasibility Study for an After-School Enrichment Program

Advancing STEM Learning Opportunities for Students with Autism Spectrum Disorder Through an Informal Robotics and Coding Program: A Feasibility Study for an After-School Enrichment Program

Shaping the future: How robot programming education changes the attitudes of robotics among rural primary school students in a developing country

Shaping the future: How robot programming education changes the attitudes of robotics among rural primary school students in a developing country

Experience-based transition to robotic surgery in an experienced program in minimally invasive hepatobiliary surgery.

The adoption of robotic techniques in liver surgery introduces significant challenges for their safe integration within hepatobiliary surgery units. This study is designed to investigate the complexities associated with establishing a robotic surgery program. Data on robotic hepatobiliary surgeries were prospectively collected from October 2021 to October 2023. Historical cohorts from the institutional experiences for comparison were hand-assisted (HALS) and purely laparoscopic procedures (PLS). Inverse probability of treatment weighting and propensity score matching were employed to compare outcomes between PLS and robotic resections. The learning curve for robotic surgeries was evaluated by the cumulative sum method. In this study, 454 patients were enrolled (113 robotic surgeries, 157 HALS, and 184 PLS). The posterosuperior segments resections were significantly higher in the robotic group (47.8%) compared to PLS (31.5%) and HALS (35.7%). There were no conversions in the robotic group, in PLS 2.7% and HALS 3.8%. The degree of difficulty according to the median of the IWATE score and IMM score was significantly higher in the robot group (p < 0.001 and p = 0.008, respectively). No significant differences in short-term outcomes were observed between robotic procedures and PLS in a matched subset of patients. Operative efficiency and blood loss improved significantly after the 75th robotic surgery patient, with high-difficulty cases (IWATE ≥ 10) incorporated from the beginning. This study suggests that robotic liver surgery in units with prior experience in minimally invasive liver surgery offers benefits, such as a lower conversion rate and a higher rate of successful difficult resections.

PUMA: Deep Metric Imitation Learning for Stable Motion Primitives

Imitation learning (IL) facilitates intuitive robotic programming. However, ensuring the reliability of learned behaviors remains a challenge. In the context of reaching motions, a robot should consistently reach its goal, regardless of its initial conditions. To meet this requirement, IL methods often employ specialized function approximators that guarantee this property by construction. Although effective, these approaches come with some limitations: 1) they are typically restricted in the range of motions they can model, resulting in suboptimal IL capabilities, and 2) they require explicit extensions to account for the geometry of motions that consider orientations. To address these challenges, we introduce a novel stability loss function that does not constrain the function approximator's architecture and enables learning policies that yield accurate results. Furthermore, it is not restricted to a specific state space geometry; therefore, it can easily incorporate the geometry of the robot's state space. Proof of the stability properties induced by this loss is provided and the method is empirically validated in various settings. These settings include Euclidean and non‐Euclidean state spaces, as well as first‐order and second‐order motions, both in simulation and with real robots. More details about the experimental results can be found at https://youtu.be/ZWKLGntCI6w.

Embodied Learning for Computational Thinking in a Mixed-Reality Context

This study examined the effects of embodied learning experiences on students’ understanding of computational thinking (CT) concepts and their ability to solve CT problems. In a mixed-reality learning environment, students mapped CT concepts, such as sequencing and loops, onto their bodily movements. These movements were later applied to robot programming tasks, where students used the same CT concepts in a different modality. By explicitly connecting embodied actions with programming tasks, the intervention aimed to enhance students’ comprehension and transfer of CT skills. Forty-four first- and second-grade students participated in the study. The results showed significant improvements in students’ CT competency and positive attitudes toward CT. Additionally, an analysis of robot programming performance identified common errors and revealed how students employed embodied strategies to overcome challenges. The effects of embodied learning and the impact of embodied learning strategies were discussed.

Older adults and robot literacy

ABSTRACT Robots have acquired roles in the provision of information and services. Older adults may interact with robots in many settings, including their homes and public places and, for example, care settings in or outside their homes. In older adults’ care, robots are used to assist their daily tasks, such as cleaning, eating, dressing, bathing, social interaction, entertainment, and medication taking. Consequently, older adults are required to have robot literacy, which has been suggested as a subset of media literacy. However, up to date, only a few researchers have defined robot literacy. In this conceptual article, we first discuss robots in the everyday lives of older adults. Second, drawing on previous studies on media literacies, digital competences, human-robot interaction and other relevant research fields, we define robot literacy as a multidimensional set of skills comprising the following seven skill dimensions: 1) awareness of robots; 2) interaction with robots; 3) understanding and evaluation of the information provided by robots; 4) understanding the data security and privacy of robots; 5) programming of robots; 6) ethical reflection; and 7) providing and receiving social support. Finally, we discuss the applicability of the concept of robot literacy and the limitations of our definition of the concept.

Rethinking Surgical Safety: Investigating the Impact of Gamified Training on Severe Flow Disruptions in Surgery

Objectives There is a need for effective and engaging training methods to enhance technical and nontechnical skills in robotic-assisted surgery (RAS), where deficiencies can compromise safety and efficiency. This study aims to evaluate the impact of a gamified team training intervention, the “RAS Olympics,” on the safety and efficiency of RAS procedures. Methods The study was conducted at a 958-bed tertiary care academic medical center in with a robust robotic surgery program. A total of 56 RAS procedures (general, urology, and gynecology) were included in the analysis, with a mix of procedure types representative of the surgical caseload at the medical center. A pretest posttest experimental design was employed, comparing the frequency and severity of flow disruptions (FD) between preintervention, postintervention without “RAS Olympics” participants, and postintervention with “RAS Olympics” participants. The “RAS Olympics” involved safety hazard identification, troubleshooting, workspace navigation, instrument retrieval, and turnover optimization. Results Postintervention cases with “RAS Olympics” participants exhibited significantly lower overall FDs compared to the postintervention control group. The reduction was particularly notable during phase 3 (surgeon on console) and in cases involving more severe FDs. Conclusions Gamified team training interventions may improve the safety and efficiency of RAS procedures. The positive outcomes underscore the potential of innovative and engaging training methods to address the evolving challenges in surgical practice, emphasizing the relevance of gamification in healthcare education. As the healthcare landscape continues to advance, incorporating such interventions may be crucial in ensuring the adaptability and competence of surgical teams.

Strategic implementation of a new robotic mitral repair program: Early experience and outcomes

BackgroundDespite numerous reported benefits of robotic mitral repair, the absolute number of procedures performed remain low in part to uncertainties about the necessary steps to launch a program. In this report, we describe our early outcomes and strategy with launching a successful new robotic mitral repair program. MethodsOur multimodal strategic plan emphasized team education, hands-on technical preparation, stepwise advancement, and careful patient selection. Consultant service analytics and team debriefings allowed for iterative improvements. ResultsBetween March 2022 and February 2024, 50 patients underwent robotic mitral repair at our institution. Average age at time of operation was 62 years with a Society of Thoracic Surgeons risk score of 0.58. Successful repairs were performed in 98% of patients. There was 1 conversion to sternotomy. There were no deaths, and there was minimal perioperative morbidity. On both predischarge and follow-up echocardiography, no patients had greater than mild mitral regurgitation. ConclusionsOur work provides a model for establishing a successful robotic mitral program. An up-front emphasis on team education, careful preparation, proper patient selection, and feedback-driven improvements can accelerate the attainment of standards set by high-volume centers.

Learning curve in robotic liver surgery: easily achievable, evolving from laparoscopic background and team-based

PurposeLimited and heterogeneous literature data necessitate a focused examination of the learning curve in robotic liver resections. This study aims to assess the learning curve of two surgeons from the same team with differing laparoscopic backgrounds. MethodSince February 2021, San Raffaele Hospital in Milan has implemented a robotic liver surgery program, performing 250 resections by three trained console surgeons. Using cumulative sum (CUSUM) analysis, the learning curve was evaluated for a Pioneer Surgeon (PS) with around 1200 laparoscopic cases and a New Generation Surgeon (NGS) with approximately 100 laparoscopic cases. Cases were stratified by complexity (38 low, 74 intermediate, 85 high). ResultsBoth PS and NGS demonstrated a learning curve for operative time after 15 low-complexity and 10 intermediate-complexity cases, with high-complexity learning curves apparent after 10 cases for PS and 18 cases for NGS. Conversion rates remained unaffected, and neither surgeon experienced increased blood loss or postoperative complications. A “team learning curve” effect in terms of operative time emerged after 12 cases, suggesting the importance of a cohesive surgical team. DiscussionThe robotic platform facilitated a relatively brief learning curve for low and intermediate complexity cases, irrespective of laparoscopic background, underscoring the benefits of team collaboration.

Initial outcomes of a robotic thoracic fellowship programme—a feasible and safe way to train the next generation

Initial outcomes of a robotic thoracic fellowship programme—a feasible and safe way to train the next generation

Robotic Surgery in Paediatric Oncology: Expanding Boundaries and Defining Relevant Indications

This article reviews the establishment and progress of the Multidisciplinary Paediatric Robotic Program in a high-volume paediatric surgery department at Hôpital Necker-Enfants Malades, Paris, France. A major foundational principle of the program was to establish a safe and secure environment for patients and staff, both pre-operatively, intra-operatively and post-operatively. This founding principle when applied systematically has allowed increasing confidence across the program and service. The robotic platform allows for precision surgery when approaching tumours, with freedom of movement adapted to meticulous vascular and organ dissection. Surgical feasibility is based on tumour characteristics, pre-operative imaging, with a focus on vascular and organ involvement, considering goals of surgery and surgical experience. Case complexity has been gradually increased (where appropriate) through an iterative process. The future of surgery is robotic, and even more so image-guided surgery, and this synergy has been instrumental when approaching tumour surgery in children. The current principles that guide application of robot-assisted surgery in paediatric tumours are presented. With this blueprint, excellent oncological outcomes can be achieved while utilising a minimally invasive approach in children with selected endocrine, neuroblastic and renal tumours.

Hierarchical robot learning method for industrial fluorescent penetrant inspection

Fluorescent Penetrant Inspection (FPI) is a Non-Destructive Testing (NDT) method, extensively used to evaluate components for identifying defects across a broad range of industries. FPI process remains a manual visual inspection, where the operator by means of fluorescent dye, that penetrates discontinuities on the component, aims to distinguish between indications that are relevant (i.e., can be associated with surface defects) and non-relevant (i.e., can be associated to insufficient wash-off, dust or other non-relevant factors). The FPI process can be decomposed into the following steps: (a) thorough visual examination of the component, (b) executing manual wiping-off of the fluorescent dye with a brush, of all areas that require interrogation for potential indications, and (c) disposition of the inspected components. The number of those areas on the component that require interrogation and hence to be wiped-off is unknown a priori of the inspection and varies depending on the condition of the part. As a result, replacing this manual wipe-off step by a robot requires tedious manual programming of an excessive number of robot paths to assure reach of the robot to the entire surface of the part as well as safe robot motion. In addition, these robot motions are part specific and thus not transferrable to other geometries of components, making scaling of this technology across manufacturing industry not possible. In this paper, we propose a hierarchical robot learning method to address the challenge of reducing manual robot programming and enable the scaling of this automated NDT technology. The proposed method integrates and fuses Deep Reinforcement Learning (DRL), Screw Linear Interpolation (ScLERP) and Learning from Demonstration (LfD), enabling an autonomous generation of brushing strokes with a six-degrees of freedom (DoF) industrial manipulator, and automating the wipe-off step of the FPI process. Using this approach, a robot learning policy is generated for the wipe-off motion in a simulated industrial robotic cell at first and then the policy is transferred to the real system for validation.

Current problems of using artificial intelligence inHR management in Kazakhstan

The article provides an understanding of artificial intelligence in the field of HR, and it is said that the digitization of the industry is reflected in the widespread use of artificial intelligence. The theory and history of the field of artificial intelligence is systematized by reviewing the works of researchers. The possibilities of using artificial intelligence are clarified and its efficiency is considered. As a result of the study, it was determined that artificial intelligence is used in four main categories in the field of HR, and it was found that there is a high demand for automated resume search, analysis, and selection services. At the same time, in order to invite candidates for a vacant position, including the identification of qualified talented employees, chatbots, Skype interviews, the use of information on online recruiting platforms such as HeadHunter, notification of vacancies at fairs, and pilot projects introduced by the state will be analyzed. The features of artificial intelligence programs introduced in the Kazakhstan labor exchange, and changes brought to the HR system, the advantages and disadvantages will be discussed. Especially as the most effective programs in the field of artificial intelligence in recent years: IBM's Deep Blue program, Alvinn program for driving, and Microsoft Application and Services Group's talking robot program, the importance of research on the specificity of programs in the field of medicine and pharmaceuticals will be analyzed and considered as examples of regional enterprises.