David A. Abbink

The aviation sector is currently facing a growing shortage of aircraft maintenance engineers, further complicated by outdated training and assessment regulations focusing on theory and time requirements rather than competencies. In response, the industry is moving towards Competency-Based Training and Assessment, which introduces a significant shift by including transversal competencies that hold strong attitudinal elements requiring assessment through direct observation of performance. This study seeks to identify the critical success factors for training and assessing these transversal competencies within aircraft maintenance vocational education and training, leveraging principles of programmatic assessment. A five-month action research study was conducted involving 21 students and three instructors, utilizing a previously developed training and assessment programme validated by industry stakeholders. The findings demonstrate that a programmatic assessment approach, integrating peer, self, and instructor assessments within individual assessment portfolios, is perceived as effective to develop and evaluate competence. Students report enhanced reflection, self-awareness, and competency performance. Key factors for success included the adoption of a coaching approach, the use of simulation and role-play, and the implementation of iterative assessments with formative feedback in supportive environments. Additionally, instructors benefited from applying the ORCA (Observe, Record, Classify, Assess) method and undertaking further training in holistic group assessment.

Robotic technologies are often proposed to relieve dull, dirty, or dangerous work, but may cause work to instead be experienced as boring, or dehumanized. Understanding the impact of robotics on the workfloor is complicated by the entanglement between emerging robotic capabilities, social dynamics, and organizational issues—which we call Worker–Robot Relations. Consequently, the impact of robotics on work is often studied in hindsight. Speculative design methodologies can facilitate alignment of robotic developments with a meaningful future of work, by creating boundary objects for communicating about current and future work practices. Making use of an unfolding artistic collaboration, we propose an experiential approach for speculating about future Worker–Robot Relations. We enabled speculative encounters between participants and robotic creatures that embody seven meta-behaviors. We abstracted these from observed behaviors in a current work context in baggage handling. We present the findings from focus groups responding to these encounters, including implications for HRI and speculative design.

Advances in haptic technologies enable rich, multi-channel haptic rendering of interactions with virtual objects during virtual reality training. However, it remains an open question whether multi-channel haptic rendering (kinesthetic and tactile) provides superior motor learning and transfer when training dynamic tasks compared to simpler, single-channel sensory information.We investigated how40 participants learned to invert and balance a virtual pendulum after training under four haptic rendering conditions: congruent kinesthetic and tactile rendering, kinesthetic rendering alone, tactile rendering alone, and no haptic rendering. Kinesthetic information was delivered through a delta robot, and tactile information through a two-dimensional skin-stretch device at the fingerpads. Participant performance was measured in catch trials during training, in short- and long-term retention trials, and with a transfer task with a shorter pendulum. Participants from all four training conditions demonstrated the ability to improve and transfer their skills. However, we observed poorer performances during catch-trials when training with reduced or absent haptic rendering compared to training with congruent kinesthetic and tactile rendering. The advantage of congruent haptic rendering over conditions lacking kinesthetic rendering was maintained during short-term retention, whereas no significant performance differences were observed between conditions in long-term retention and the transfer task. These results suggest that congruent haptic rendering benefits the task’s early learning by supporting the generation of internal models of the task dynamics, with kinesthetic rendering playing a major role. Overall, our findings highlighting the potential benefits of multi-channel haptic rendering to accelerate virtual reality training.

Force perturbations during gait training can increase movement variability, which may support motor exploration and learning. However, when such perturbations are delivered through a robotic exoskeleton, they can also reduce perceived exoskeleton transparency, potentially hindering user acceptance. We tested whether visualizing continuous upper leg-level perturbations in immersive virtual reality (VR) could preserve their variability-enhancing effect while mitigating the cost to perceived transparency in a pelvis-centered walking task. Thirty healthy adults walked on a treadmill while wearing a robotic exoskeleton and performed a ball-in-cup task, requiring continuous mediolateral control of the pelvis. Participants trained under one of three conditions: Control (no perturbations), Perturbation (continuous noise-like multisine forces applied at the thighs), or Perturbation + Visual (same forces with a real-time, body-referenced force-beam visualization). Step-width variability was evaluated during Training. Task performance, intrinsic motivation, and perceived transparency were assessed across Baseline, Training, Retention, and a faster-speed Transfer test (120% of preferred speed). Both perturbation conditions significantly increased step-width variability during Training relative to Control, with no detectable difference between Perturbation and Perturbation + Visual. Task performance improved from Baseline to Retention and Transfer across all conditions, with no significant differences across conditions. Motivation did not differ between conditions either. Critically, perceived transparency decreased only in the non-visualized Perturbation condition and remained stable in both Control and Perturbation + Visual. Our results show that continuous leg-level perturbations reliably enrich lateral gait variability and that simple visual force cues can prevent a perceived transparency cost without compromising the variability manipulation. Future work should test adaptive dosing, multi-session training, and clinical cohorts with impaired lateral stability.

Arm weight support (AWS) from upper-limb exoskeletons aids training by reducing fatigue and increasing the movement workspace, especially in people with acquired brain injury (ABI). Simultaneously, task-specific training could be enabled via haptic rendering, simulating the interaction forces with tangible virtual objects. However, how AWS affects somatosensory perception, such as the perception of the weight of haptically rendered virtual objects, is unclear. We therefore conducted a psychophysics experiment with 40 participants investigating the effect of partial AWS on virtual object weight perception during dynamic lifting. Participants performed the lifting task under two conditions: with and without AWS, where AWS compensated for 50% of their arm weight. An upperlimb exoskeleton equipped with a haptic hand module provided both AWS and haptic rendering of virtual objects. The virtual objects and a representation of the participant's arm and hand were visualized using immersive virtual reality.Weight perception was assessed using just-noticeable differences (JNDs). To evaluate the experimental setup, participants' estimated elbow torques were analyzed. Additionally, subjective measures of task load, motivation, and user experience with the exoskeleton were collected to explore potential secondary factors influencing weight perception. We found that partial AWS effectively reduced elbow load during dynamic lifting compared to no-AWS. The JND was not significantly different between conditions, suggesting that AWS does not hamper weight discrimination. However, participants with a larger self-reported sense of control over their movement and those who reported that the robot followed their movements more closely were associated with better weight perception. Our findings suggest that including haptic rendering into exoskeletons that provide partial AWS could be a viable solution to provide task-specific training.

BACKGROUND There is great potential in technologies, such as robotics and artificial intelligence, to support nurses. However, their effective adoption and implementation is hampered by limited nurse involvement and insufficient transdisciplinary collaboration. OBJECTIVE (1) Better understanding nurses’ current challenges and needs to identify opportunities for technological innovations that can address these challenges and needs; (2) Establishing a transdisciplinary process with ethnographic methods and participatory design for co-creating innovation opportunities that matter to nurses and are technically feasible and organizationally viable. METHODS We enacted transdisciplinary collaboration by purposefully combining an ethnographic approach and participatory design workshops at a Dutch university hospital. Participants included nurses, nurse managers, organizational leaders, health technology experts, and academic researchers representing at least five different disciplines. First, we employed ethnographic methods to gain a detailed understanding of nurses’ daily work. This includes observations of bedside nurses during 16 full shifts, and interviews with nine nurses and managers about their experiences and technology needs. Following this, we organized 12 participatory design workshops to deepen our understanding of nursing workflows and subtasks: (1) three journey mapping sessions with nurses from four wards to map detailed task flows during shifts; (2) eight opportunity identification sessions with nurses and multiple stakeholders to identify 20 innovation opportunities; and (3) an impact versus effort workshop for shared decision-making on which innovation opportunities to prioritize. These workshops promoted shared learning among nurses and nursing scientists, organizational managers, technology practitioners, robotics engineers, designers, health technology experts, and researchers. RESULTS Based on the observations and interviews, we identified four overarching categories that capture the complexity of nursing work, and its associated technological needs: (1) nursing tasks with an unpredictable nature of the work and the need for constant decision-making and prioritization; (2) current tools nurses use and expressed needs for future ones; (3) affects and emotions associated with frequent interruptions during work, the use of technology and people at work; and (4) organizational management and collaboration across hospital disciplines. These findings guided the participatory design workshops, resulting in the identification of 20 potential innovation opportunities. The subsequent “Impact versus Effort” workshop collectively prioritized high-impact, high-effort areas such as reducing documentation load, looking for and fetching materials, and minimizing alarm interruptions. CONCLUSIONS This study highlights the complexity of nursing work and the importance of addressing nurses’ technological needs through a transdisciplinary process with ethnographic methods and participatory design. Empowering nurses as key and active partners in innovation and research, supported by organizational readiness and education, is essential for developing and implementing technologies that improve care quality, efficiency, and workforce sustainability. This established approach can help to empower nurses in prioritizing, developing, and implementing viable innovations that truly enhance job satisfaction and efficiency in nursing. CLINICALTRIAL NA.

In this letter, we investigate whether classical function allocation—the principle of assigning tasks to either a human or a machine—holds for physical Human-Robot Collaboration, which is important for providing insights for Industry 5.0 to guide how to best augment rather than replace workers. This study empirically tests the applicability of Fitts' List within physical Human-Robot Collaboration, by conducting a user study (N=26, within-subject design) to evaluate four distinct allocations of position/force control between human and robot in an abstract blending task. We hypothesize that the function in which humans control the position achieves better performance and receives higher user ratings. When allocating position control to the human and force control to the robot, compared to the opposite case, we observed a significant improvement in preventing overblending. This was also perceived better in terms of physical demand and overall system acceptance, while participants experienced greater autonomy, more engagement and less frustration. An interesting insight was that the supervisory role (when the robot controls both position and force) was rated second best in terms of subjective acceptance. Another surprising insight was that if position control was delegated to the robot, the participants perceived much lower autonomy than when the force control was delegated to the robot. These findings empirically support applying Fitts' principles to static function allocation for physical collaboration, while also revealing important nuanced user experience trade-offs, particularly regarding perceived autonomy when delegating position control.

The use of robots in industrial settings continues to grow, driven by the need to address complex societal challenges such as labor shortages, aging populations, and ever-increasing production demands. In this abstract, we advocate for (and demonstrate) a transdisciplinary approach when considering robotics in the workplace. Transdisciplinarity emphasizes the integration of academic research with pragmatic expertise and embodied experiential knowledge, that prioritize values such as worker wellbeing and job attractiveness. In the following, we describe an ongoing multi-pronged effort to explore the potential of collaborative robots in the context of airplane engine repair and maintenance operations.