Background: Robotic devices have shown promise in supporting motor (re)learning. However, there is a limited understanding of how personality traits influence the effectiveness of robot-aided training strategies. Methods: We conducted a motor learning experiment with 40 unimpaired participants who trained to control a virtual pendulum using a robotic haptic device. Before the experiment, we assessed personality traits including the perceived control over life events (Locus of Control), the tendency to turn challenges into engaging activities (Transform of Challenge), and other subscales from Autotelic and Hexad gaming style questionnaires. Participants were divided into two groups, one receiving haptic guidance during training and a second one without assistance. Short- and long-term retention was assessed, and relationships between personality traits, performance metrics, and human-robot interaction metrics were analyzed. Results: Participants with high Transform of Challenge or external Locus of Control characteristics who received physical guidance during training reduced the human-robot interaction forces to a lesser extent compared to the ones who did not receive guidance. Additionally, participants with a high Free Spirit gaming style showed greater sensitivity to how their perception of the guidance affected their performance during the retention phases. Conclusion: Our findings suggest that autotelic personality, Locus of Control, and gaming style modulate motor learning outcomes during robotic-assisted training, affecting both performance and human-robot interaction metrics. This highlights the potential of integrating personality-based adaptations in robot-aided rehabilitation protocols to enhance performance and motor (re)learning. Future works should explore the relationship between personality traits and psychological states (e.g., perceived difficulty, attention) across diverse tasks and guidance methods in clinical populations.

Combining biomechanical modeling with robotic physiotherapy is a promising direction to provide real-time insights during the rehabilitation of patients with musculoskeletal injuries, such as rotator-cuff tears. One aspect is to prevent re-injuries caused by high strain in the injured tissues while allowing patients to perform the required rehabilitation exercises. In this paper, we propose a novel shared control method for robots to limit unsafe patient movements, through physical guidance based on a strain-space representation of the human rotator cuff. The method provides motion corrections through two complementary predictive modules. The first module exerts a lower degree of intervention and is analogous to rumble strips or speed bumps for cars on the road. In this case, an impedance controller induces variable damping to slow down the patient's movement when a danger zone is approached. The second module produces a higher degree of intervention and is analogous to lane-assist in cars. In this case, the robot plans an optimal deflection trajectory and temporarily takes over control of the movement to avoid an unsafe situation. We performed experiments with a healthy participant acting as a patient and evaluated the effect of different human-robot interaction modalities on the resulting human movement in terms of avoidance of high-strain areas of the rotator-cuff tendons and contact forces exchanged.

Robotic Ultrasound Systems (RUSS) provide a promising solution to reduce operator dependency, alleviate physical strain, and meet the growing demand for ultrasound procedures. However, their clinical applicability remains limited by their inability to adapt to dynamic patient movements and tissue deformations during scans. This work introduces a novel framework that leverages Laplacian Trajectory Editing (LTE) for real-time adaptation of scan trajectories in response to both rigid and non-rigid patient movements. it integrates a RGB-D camera to capture surface point clouds, which are processed to estimate displacements between consecutive frames. These displacements define anchor points for LTE-based trajectory adaptations, ensuring smooth motion while preserving local trajectory properties. This approach is validated through experiments spanning rigid phantom movements, generalization across differently shaped phantoms, and non-rigid human arm motion. Adaptation accuracy is quantified by comparing adapted trajectories to a ground-truth reference, with root mean squared errors averaging 0.026 0.012 m in non-rigid scenarios. Real-time trajectory adaptation is achieved, with an average LTE adaptation processing time of 373 ms per trial. Furthermore, our implementation achieved low tracking errors across all conditions while maintaining a high success rate in diverse movement scenarios. These results demonstrate the feasibility of LTE for real-time trajectory adaptation in ultrasound scanning, offering a pathway to more autonomous and clinically viable RUSS implementations.

This paper presents a multi-modal dynamic workspace re-indexing method for addressing operator ergonomics and workspace limitations. The proposed method has two interactive modes: pose-to-pose mode, which is active when the operator is within an ergonomic workspace of comfortable arm postures, and ergonomic workspace drift mode, which activates after the operator makes an excursion beyond the boundaries of the ergonomic workspace when trying to reach more distant targets with the remote robot. In the ergonomic workspace drift mode, the operator temporarily stays slightly outside these boundaries, while the offset between the local and remote workspace drifts with a velocity proportional to the excursion distance. This dynamically re-indexes the remote workspace toward the distant target, and the operator can remain in a comfortable posture while the remote robot moves toward the intended target where the task is. To construct the ergonomic workspace, we employed the Rapid Upper Limb Assessment method. To validate the proposed method, we conducted experiments on a teleoperation setup involving a Force Dimension Sigma7 haptic device controlling a Kuka LBR iiwa robotic arm. The results show that the proposed controller successfully addresses workspace limitations by dynamically reindexing the follower's workspace towards target objects, while maintaining good operator ergonomics.

Despite recent advancements in physical humanrobot collaboration, measuring and distinguishing between forces applied by humans and robots remains challenging, limiting our understanding of force dynamics during collaboration. Our proposed solution addresses this gap with a low-cost, lightweight design that integrates directly at the robot endeffector level. The interface employs a three-ring mechanical structure with strategically positioned load cells and a Sarrus mechanism to constrain movement to the z-axis only, enabling tool mounting for real-world collaborative tasks such as blending or sanding operations. Validation experiments demonstrate excellent force decoupling capabilities with minimal crossinterference, achieving Weighted Root Mean Squared Errors of 0.14 N for robot-applied forces and 0.08 N for human-applied forces compared to ground truth measurements in steadystate for loads ranging from 0 N up to 23 N. The Maximum Absolute Error in these experiments is 0.33 N, confirming high measurement accuracy. This affordable and integrated solution lowers the threshold for employing decoupled force sensing in collaborative tasks, making it more accessible for investigating force dynamics and developing adaptive control strategies in both research and practical applications of physical humanrobot collaboration.

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.