In Industry 5.0, cognitive robots and workers will engage in evolving and reciprocal relations, which we call worker-robot relationships (WRRs). To enable evidence-based work futures with workers, we must co-develop WRRs and understand their impact on work, workers, management, and society. To this end, we posit that the HRI field should work beyond disciplines and include value-driven and plural perspectives through transdisciplinary research done with and for workers. However, WRRs and transdisciplinarity pose unique technical, design, and methodological challenges yet to be explored. We propose a workshop to engage the HRI community working on Industry 5.0, aiming at 1) taking stock of current WRR-related challenges in relevant disciplines, 2) collectively kick-off the exploration of a joint research agenda, 3) preliminary examining if and how transdisciplinarity could help the HRI community, and 4) start discussing how to deal with such complex knowledge integration in practice.

Skill propagation among robots without human involvement can be crucial in quickly spreading new physical skills to many robots. In this respect, it is a good alternative to pure reinforcement learning, which can be time-consuming, or learning from human demonstration, which requires human involvement. In the latter case, there may not be enough humans to quickly spread skills to many robots. However, propagation among robots without direct human supervision can result in robotic skills mutating from the original source. This can be beneficial when better skills might emerge or when a new skill is obtained to be used for other similar tasks. However, it can also be dangerous in terms of task execution safety. This letter studies the mutation of a robotic skill when it is propagated from one robot to another during a physically collaborative task. We chose the collaborative sawing task as a study case since it involves complex two-agent physical interaction/coordination and because its periodic nature can facilitate repetitive learning. The study employs periodic Dynamic Movement Primitives and Locally Weight Regression to encode and learn the motion and impedance required to execute the task. To explore what influences mutation, we varied several control and environment conditions such as the maximum stiffness, robot base position, friction coefficient of the sawed object, and movement period. The results showed that the skill varied over propagation steps and we identified several key aspects of mutation such as movement length, movement offset, and trajectory shape. Based on the results we identified possible benefits (skill mutations useful for different settings or different tasks, and energy efficiency) and dangers (high forces and skill mutations becoming useless for the original task) of the mutation.

In this work, we explore using computational musculoskeletal modeling to equip an industrial collaborative robot with awareness of the internal state of a patient to safely deliver physical therapy. A major concern of robot-mediated physical therapy is that robots may unwittingly injure patients. For patients with shoulder injuries this typically means the risk of tearing a rotator-cuff muscle tendon. Risk of reinjury hampers both human and robot therapists and it is the main reason for conservative physical therapy. Advances in human musculoskeletal modeling, however, can equip robots with additional perception of potential reinjury risks. While the ultimate goal is to improve the safety, range-of-motion and activity that patients receive through robot-mediated therapy, the aim of this letter is to develop and test a framework that enables the robot to understand the state of the patient and to execute physical therapy movements that demonstrate low injury risk and achieve a large range-of-motion in human subjects. We build on prior work in human-robot interaction via impedance control, but take robot awareness of the human to the next level by including and manipulating a musculoskeletal model in parallel to the patient. Taking the most common shoulder impairments (i.e., rotator-cuff tears) as an example, we demonstrate planned, model-based trajectories that minimize strain in these muscles and corresponding robot-mediated movements on healthy subjects. Our experiments suggest that musculoskeletal awareness is a promising approach to plan and deliver therapeutic movements that are safe and effective via an industrial robot.

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^circ/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.

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^circ/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.

Objective: Robotic endoscopes have the potential to dramatically improve endoscopy procedures, however current attempts remain limited due to mobility and sensing challenges and have yet to offer the full capabilities of traditional tools. Endoscopic intervention (e.g., biopsy) for robotic systems remains an understudied problem and must be addressed prior to clinical adoption. This paper presents an autonomous intervention technique onboard a Robotic Endoscope Platform (REP) using endoscopy forceps, an auto-feeding mechanism, and positional feedback. Methods: A workspace model is established for estimating tool position while a Structure from Motion (SfM) approach is used for target-polyp position estimation with the onboard camera and positional sensor. Utilizing this data, a visual system for controlling the REP position and forceps extension is developed and tested within multiple anatomical environments. Results: The workspace model demonstrates accuracy of 5.5% while the target-polyp estimates are within 5 mm of absolute error. This successful experiment requires only 15 seconds once the polyp has been located, with a success rate of 43% using a 1 cm polyp, 67% for a 2 cm polyp, and 81% for a 3 cm polyp. Conclusion: Workspace modeling and visual sensing techniques allow for autonomous endoscopic intervention and demonstrate the potential for similar strategies to be used onboard mobile robotic endoscopic devices. Significance: To the authors' knowledge this is the first attempt at automating the task of colonoscopy intervention onboard a mobile robot. While the REP is not sized for actual procedures, these techniques are translatable to devices suitable for in vivo application.

Objective: Robotic endoscopes have the potential to dramatically improve endoscopy procedures, however current attempts remain limited due to mobility and sensing challenges and have yet to offer the full capabilities of traditional tools. Endoscopic intervention (e.g., biopsy) for robotic systems remains an understudied problem and must be addressed prior to clinical adoption. This paper presents an autonomous intervention technique onboard a Robotic Endoscope Platform (REP) using endoscopy forceps, an auto-feeding mechanism, and positional feedback. Methods: A workspace model is established for estimating tool position while a Structure from Motion (SfM) approach is used for target-polyp position estimation with the onboard camera and positional sensor. Utilizing this data, a visual system for controlling the REP position and forceps extension is developed and tested within multiple anatomical environments. Results: The workspace model demonstrates accuracy of 5.5% while the target-polyp estimates are within 5 mm of absolute error. This successful experiment requires only 15 seconds once the polyp has been located, with a success rate of 43% using a 1 cm polyp, 67% for a 2 cm polyp, and 81% for a 3 cm polyp. Conclusion: Workspace modeling and visual sensing techniques allow for autonomous endoscopic intervention and demonstrate the potential for similar strategies to be used onboard mobile robotic endoscopic devices. Significance: To the authors' knowledge this is the first attempt at automating the task of colonoscopy intervention onboard a mobile robot. While the REP is not sized for actual procedures, these techniques are translatable to devices suitable for in vivo application.