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While recent advancements in motor learning have emphasized the critical role of systematic task scheduling in enhancing task learning, the heuristic design of task schedules remains predominant. Random task scheduling can lead to sub-optimal motor learning, whereas performance-based scheduling might not be adequate for complex motor skill acquisition. This paper addresses these challenges by proposing a model-based approach for online skill estimation and individualized task scheduling in de-novo (novel) motor learning tasks. We introduce a framework utilizing a personalized human motor learning model and particle filter for skill state estimation, coupled with a stochastic nonlinear model predictive control (SNMPC) strategy to optimize curriculum design for a high-dimensional motor task. Simulation results show the effectiveness of our framework in estimating the latent skill state, and the efficacy of the framework in accelerating skill learning. Furthermore, a human subject study shows that the group with the SNMPC-based curriculum design exhibited expedited skill learning and improved task performance. Our contributions offer a pathway towards expedited motor learning across various novel tasks, with implications for enhancing rehabilitation and skill acquisition processes. 

Integrating self-healing materials with structural health monitoring (SHM) represents a significant advancement in materials science and engineering. In applications such as aerospace, self-healing composites with SHM can detect and repair damage autonomously, enhancing safety and reducing maintenance time, which significantly improves structural durability by maintaining integrity and extending material service life. This research employs an experimental approach to validate the self-healing process of self-healing metal matrix composites reinforced with shape memory alloy fibers, by utilizing lead zirconate titanate (PZT) piezoelectric transducers mounted on the surface of the composite. A three-point bending test is used to induce damage in the metal matrix composites by applying a load at its midpoint while supporting it at both ends; then, self-healing is used to return the specimen to its original state. The ultrasonic signals from the PZT sensor were compared at three stages: pristine state, during bending (i.e., damage), and after the healing process, to evaluate the effectiveness of the healing process and health monitoring technique adopted for this study. Real-time monitoring using digital image correlation, laser profilometry, and mechanical testing was used to validate the damaged/healed signal state—demonstrated by improved recovered signal amplitude, reduced scatter energy, a notable decrease in damage indices root-mean-square deviation, normalized scatter energy, and stabilized wave propagation. The successful self-healing composite design and health monitoring results confirm the restoration of the specimen, which regained 84% of its initial shape deformation at 135 °C for 30 min (i.e., during the first step of healing). Complete shape restoration was achieved by raising the temperature to 150 °C for 90 min (during the second step of healing), recovering approximately 96% of the original flexural strength after healing. 

Abstract Using the context of trajectory estimation and tracking for multirotor unmanned aerial vehicles (UAVs), we explore the challenges in applying high-gain observers to highly dynamic systems. The multirotor will operate in the presence of external disturbances and modeling errors. At the same time, the reference trajectory is unknown and generated from a reference system with unknown or partially known dynamics. We assume the only measurements that are available are the position and orientation of the multirotor and the position of the reference system. We adopt an extended high-gain observer (EHGO) estimation framework to estimate the unmeasured multirotor states, modeling errors, external disturbances, and the reference trajectory. We design a robust output feedback controller for trajectory tracking that comprises a feedback linearizing controller and the EHGO. The proposed control method is rigorously analyzed to establish its stability properties. Finally, we illustrate our theoretical results through numerical simulation and experimental validation in which a multirotor tracks a moving ground vehicle with an unknown trajectory and dynamics and successfully lands on the vehicle while in motion. 

Abstract We examine how a human–robot interaction (HRI) system may be designed when input–output data from previous experiments are available. Our objective is to learn an optimal impedance in the assistance design for a cooperative manipulation task with a new operator. Due to the variability between individuals, the design parameters that best suit one operator of the robot may not be the best parameters for another one. However, by incorporating historical data using a linear autoregressive (AR-1) Gaussian process, the search for a new operator’s optimal parameters can be accelerated. We lay out a framework for optimizing the human–robot cooperative manipulation that only requires input–output data. We characterize the learning performance using a notion called regret, establish how the AR-1 model improves the bound on the regret, and numerically illustrate this improvement in the context of a human–robot cooperative manipulation task. Furthermore, we show how our approach’s input–output nature provides robustness against modeling error through an additional numerical study. 

Conventional approaches to enhance movement coordination, such as providing instructions and visual feedback, are often inadequate in complex motor tasks with multiple degrees of freedom (DoFs). To effectively address coordination deficits in such complex motor systems, it becomes imperative to develop interventions grounded in a model of human motor learning; however, modeling such learning processes is challenging due to the large DoFs. In this paper, we present a computational motor learning model that leverages the concept of motor synergies to extract low-dimensional learning representations in the high-dimensional motor space and the internal model theory of motor control to capture both fast and slow motor learning processes. We establish the model’s convergence properties and validate it using data from a target capture game played by human participants. We study the influence of model parameters on several motor learning trade-offs such as speed-accuracy, exploration-exploitation, satisficing, and flexibility-performance, and show that the human motor learning system tunes these parameters to optimize learning and various output performance metrics. 

Using a dual-task paradigm, we explore how robot actions, performance, and the introduction of a secondary task influence human trust and engagement. In our study, a human supervisor simultaneously engages in a target-tracking task while supervising a mobile manipulator performing an object collection task. The robot can either autonomously collect the object or ask for human assistance. The human supervisor also has the choice to rely on or interrupt the robot. Using data from initial experiments, we model the dynamics of human trust and engagement using a linear dynamical system (LDS). Furthermore, we develop a human action model to define the probability of human reliance on the robot. Our model suggests that participants are more likely to interrupt the robot when their trust and engagement are low during high-complexity collection tasks. Using Model Predictive Control (MPC), we design an optimal assistance-seeking policy. Evaluation experiments demonstrate the superior performance of the MPC policy over the baseline policy for most participants. 

Cognitive rehabilitation, STEM (science, technology, engineering, and math) skill acquisition, and coaching games such as chess often require tutoring decision-making strategies. The advancement of AI-driven tutoring systems for facilitating human learning requires an understanding of the impact of evaluative feedback on human decision-making and skill development. To this end, we conduct human experiments using Amazon Mechanical Turk to study the influence of evaluative feedback on human decision-making in sequential tasks. In these experiments, participants solve the Tower of Hanoi puzzle and receive AI-generated feedback while solving it. We examine how this feedback affects their learning and skill transfer to related tasks. Additionally, treating humans as noisy optimal agents, we employ maximum entropy inverse reinforcement learning to analyze the effect of feedback on the implicit human reward structure that guides their decision making. Lastly, we explore various computational models to understand how people incorporate evaluative feedback into their decision-making processes. Our findings underscore that humans perceive evaluative feedback as indicative of their long-term strategic success, thus aiding in skill acquisition and transfer in sequential decision-making tasks. Moreover, we demonstrate that evaluative feedback fosters a more structured and organized learning experience compared to learning without feedback. Furthermore, our results indicate that providing intermediate goals alone does not significantly enhance human learning outcomes.