This full-day workshop is dedicated to mapping and building a community toward shaping sustainable futures of robot-assisted work, with and for workers. The program encompasses lightning keynotes from HRI experts, talks from selected participants bringing case studies, and a hands-on mapping activity for understanding the landscape of robotics innovation at work - in order to foster knowledge exchange and learning. The workshop culminates in a facilitated conversation with workers and managers from a particular case study, providing real-world insights into the challenges, complexities and opportunities for our HRI community in shaping the future of work. With this rich set of activities, the program aims to bridge the gap between academics and practitioners, and to promote a holistic understanding of our roles in shaping the future of work, with particular attention to systemic challenges and value-driven approaches. The workshop will conclude with a synthesis of key takeaways and potential directions for future research and community building.

In this work, we propose a method of capturing the patient’s discomfort during robotic shoulder physiotherapy, creating "discomfort maps". These maps depict the personalized distribution of discomfort that each patient perceived across their shoulder range of motion, facilitating both robotic devices and human therapists to account for patient-specific characteristics during the therapeutic process. Our system enables a patient to communicate and map discomfort in space and time during movement via a handheld push-button device, while interacting with a robotic physical therapy device capable of moving the patient and estimating their pose. We validated our method through human factors experiments simulating shoulder physiotherapy sessions with 10 healthy participants. To avoid the risk of injury to the participants and to allow for ground truth map information, we emulate perceived discomfort via an auditory signal. Our experimental apparatus enabled participants to reconstruct synthetic discomfort maps, demonstrating the feasibility of automatically capturing and storing patient discomfort during robotic physiotherapy.

With significant progress being made toward improving endoscope technology such as capsule endoscopy and robotic endoscopy, the development of advanced strategies for manipulating, controlling, and more generally, easing the accessibility of these devices for physicians is an important next step. This article presents an autonomous navigation strategy for use in endoscopy, utilizing a state-dependent region estimation approach to allow for multimodal control design. This region estimator is evaluated for its accuracy in predicting yaw angle of the camera relative to the lumen center, and for estimating the location of the camera based on overall haustra morphology within the colon. To assess the utility of this region estimator, multimodal control is used to allow for autonomous navigation of the Endoculus, a robotic capsule endoscope, within a benchtop, to-scale, simulated colon. The estimation approach is presented and tested, demonstrating successful tracking of fixed velocity rotations at speeds up to 40°/s and allowing for curve anticipation approximately 10 cm before entering a curved section of the simulator. Finally, the multimodal control strategy utilizing this estimator is tested within the simulator over a variety of anatomic configurations. This strategy proves successful for navigation in both straight sections of this simulator and in tightly curved sections as small as 8 cm radius of curvature, with average velocities reaching 2.61 cm/s in straight sections and 0.99 cm/s in curved sections.

This paper presents a new open-source dataset with ground truth position in a simulated colon environment to promote development of real-time feedback systems for physicians performing colonoscopies. Four systems (DSO, LSD-SLAM, SfMLearner, ORB-SLAM2) are tested on this dataset and their failures are analyzed. A data collection platform was fabricated and used to take the dataset in a colonoscopy training simulator that was affixed to a flat surface. The noise in the ground truth positional data induced from the metal in the data collection platform was then characterized and corrected. The Absolute Trajectory RMSE Error (ATE) and Relative Error (RE) metrics were performed on each of the sequences in the dataset for each of the Simultaneous Localization And Mapping (SLAM) systems. While these systems all had good performance in idealized conditions, more realistic conditions in the harder sequences caused them to produce poor results or fail completely. These failures will be a hindrance to physicians in a real-world scenario, so future systems made for this environment must be more robust to the difficulties found in the colon, even at the expense of trajectory accuracy. The authors believe that this is the first open-source dataset with groundtruth data displaying a simulated in vivo environment with active deformation, and that this is the first step toward achieving useful SLAM within the colon. The dataset is available at www.colorado.edu/lab/amtl/datasets.

Robotic capsule endoscopes (RCEs) are being widely investigated to improve the state of various endoscopy procedures. This article presents the novel design of a multi-DOF sensor-enabled RCE for colonoscopies (Endoculus) and evaluates porcine in vivo and ex vivo performance. The novelty of the design includes a custom “double-worm” drive that removes axial gear forces while reducing radial moments, and the full parameterization of gear geometries allows for size minimization via an optimization routine over design constraints. Two independently controlled motors drive micro-pillared treads above and below the device allowing for two-degrees of freedom (2-DOF) skid-steering, even in a collapsed lumen. The Endoculus contains all functionality of a traditional endoscope: a camera, adjustable light emitting diodes (LEDs), channels for insufflation and irrigation, and a tool port for endoscopy instruments (e.g., forceps, snares, etc.). Additionally, the Endoculus carries an inertial measurement unit, magnetometer, motor encoders, and motor current sensors to aid in future autonomy strategies. Porcine surgical evaluation demonstrated locomotion up to 40 mm/s on the colon mucosa, 2-DOF steering, the ability to traverse haustral folds, and functionality of endoscopy tools. This platform will enable future validation of feedback control, localization, and mapping algorithms in the unconventional in vivo environment.

Capsule endoscopes have gained popularity over the last decade as minimally invasive devices for diagnosing gastrointestinal abnormalities such as colorectal cancer. While this technology offers a less invasive and more convenient alternative to traditional scopes, these capsules are only able to provide observational capabilities due to their passive nature. With the addition of a reliable mobility system and a real-time navigation system, capsule endoscopes could transform from observational devices into active surgical tools, offering biopsy and therapeutic capabilities and even autonomous navigation in a single minimally invasive device. In this work, a vision system is developed to allow for autonomous lumen center tracking and haustral fold identification and tracking during colonoscopy. This system is tested for its ability to accurately identify and track multiple haustral folds across many frames in both simulated and in vivo video, and the lumen center tracking is tested onboard a robotic endoscope platform (REP) within an active simulator to demonstrate autonomous navigation. In addition, real-time localization is demonstrated using open source ORB-SLAM2. The vision system successfully identified 95.6% of Haustral folds in simulator frames and 70.6% in in vivo frames and false positives occurred in less than 1% of frames. The center tracking algorithm showed in vivo center estimates within a mean error of 6.6% of physician estimates and allowed for the REP to traverse 2 m of the active simulator in 6 minutes without intervention.