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Abstract Force and moment measurements are critical for powered ankle-foot prostheses, with over 73% of prototypes incorporating such sensors. Hardware complexity, compatibility limitations, and cost barriers currently limit widespread clinical adoption. This work presents four contributions using the commercial strain gauge based instrumented pyramid adapter Europa+: (1) hardware integration compatible with standard prosthetic components and minimal added bulk (275 g, 37.5 mm height); (2) an adaptive zero drift compensation algorithm achieving stable calibration within 2–3 steps for continuous long-term operation; (3) physics-informed linear regression models for real-time ankle force and moment estimation; and (4) experimental validation with 8 nonamputee participants using a passive prosthesis, and a preliminary single-participant evaluation with a two-degree-of-freedom (2DOF) powered ankle-foot prosthesis in variable impedance closed-loop operation. Results demonstrate exceptional axial force estimation (RMSE=47 ± 20 N, R2=0.97 ± 0.03) and strong dorsi-plantar (DP) moment estimation (RMSE=5.7 ± 2.3 Nm, R2=0.87 ± 0.06) across passive prosthesis participants, and these remain valid for the powered prosthesis. Inversion-eversion (IE) moment estimation achieves RMSE=0.8 ± 0.2 Nm and R2=0.44 ± 0.16 on passive data, but performance improves when model parameters are derived from powered prosthesis gait with larger active IE range. This approach establishes a generalizable methodology applicable to strain gauge based instrumented pyramid adapters, offering a practical alternative to custom sensors while significantly reducing barriers to clinical implementation of ankle dynamics estimation in prosthetic applications. 

The interaction and collaboration between humans and multiple robots represent a novel field of research known as human multirobot systems. Adequately designed systems within this field allow teams composed of both humans and robots to work together effectively on tasks, such as monitoring, exploration, and search and rescue operations. This article presents a deep reinforcement learning-based affective workload allocation controller specifically for multihuman multirobot teams. The proposed controller can dynamically reallocate workloads based on the performance of the operators during collaborative missions with multirobot systems. The operators' performances are evaluated through the scores of a self-reported questionnaire (i.e., subjective measurement) and the results of a deep learning-based cognitive workload prediction algorithm that uses physiological and behavioral data (i.e., objective measurement). To evaluate the effectiveness of the proposed controller, we conduct an exploratory user experiment with various allocation strategies. The user experiment uses a multihuman multirobot CCTV monitoring task as an example and carry out comprehensive real-world experiments with 32 human subjects for both quantitative measurement and qualitative analysis. Our results demonstrate the performance and effectiveness of the proposed controller and highlight the importance of incorporating both subjective and objective measurements of the operators' cognitive workload as well as seeking consent for workload transitions, to enhance the performance of multihuman multirobot teams. 

Abstract As the demand for assistive technologies in healthcare grows, there is a unique opportunity to engage underrepresented students in STEM education. This paper presents the design and evaluation of a co-robotics curriculum that integrates robotics, computer science, and assistive technology to inspire high school students, particularly from underrepresented backgrounds, to pursue STEM careers. The curriculum focuses on building and programming a robotic arm (the “Neupulator”) controlled by bio-signals, including electromyography and accelerometers, to simulate human–robot interaction for enhancing quality of life. Utilizing the 6E instructional model, the curriculum was implemented across multiple phases in schools, with iterative improvements informed by qualitative data from teacher interviews, classroom observations, and professional development sessions. Key findings highlight the curriculum’s success in engaging students through hands-on activities, while other challenges like hardware complexity and delicate electronics were identified and addressed to optimize learning experiences. The study contributes to the growing field of collaborative robotics education by offering an accessible, low-cost curriculum that aligns robotics education with real-world applications, with the potential to foster interest in STEM fields like biomedical engineering and robotics. Future work will assess the curriculum's long-term impact on student motivation, self-efficacy, and career aspirations. 

Abstract Mobile robots with manipulation capability are a key technology that enables flexible robotic interactions, large area covering and remote exploration. This paper presents a novel class of actuation-coordinated mobile parallel robots (ACMPRs) that utilize parallel mechanism configurations and perform hybrid moving and manipulation functions through coordinated wheel actuators. The ACMPRs differ with existing mobile manipulators by their unique combination of the mobile wheel actuators and the parallel mechanism topology through prismatic joint connections. Common motion of the wheels will provide mobile function while their relative motion will actuate the parallel manipulation function. This new concept reduces actuation requirement and increases manipulation accuracy and mobile motion stability through coordinated and connected wheel actuators comparing with existing mobile parallel manipulators. The relative wheel location on the base frame also enables a reconfigurable base size with variable moving stability on the ground. The basic concept and general type synthesis are introduced and followed by kinematics and inverse dynamics analysis of a selected three limb ACMPR. A numerical simulation also illustrates the dynamics model and the motion property of the new mobile parallel robot (MPR) followed by a prototype-based experimental validation. The work provides a basis for introducing this new class of robots for potential applications in surveillance, industrial automation, construction, transportation, human assistance, medical applications, and other operations in extreme environment such as nuclear plants, Mars, etc.