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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. 

Abstract With advances in materials and manufacturing techniques, recent years have seen a number of conductive composite materials that exhibit pronounced strain-dependent electrical resistivity, allowing them to be used for embedded, cost-effective strain sensing in various applications. The strain-resistivity relationship of these materials, however, is often highly nonlinear and dynamic, posing challenges for effective use of such strain sensors. In this paper, a computationally efficient scheme is proposed for compensating the nonlinear, dynamic strain-resistance behavior of a soft conductive rubber using a time delay neural network. The accuracy and feasibility of the technique is evaluated with a soft robotic arm incorporating three strain sensors for proprioception. Experimental results show that the sensing scheme is able to predict both the tip position and the shape of the robotic manipulator, achieving an average tip positional error of less than 4% relative to the total length of the manipulator. 

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. 

Abstract The inherent low stiffness in soft robots makes them preferable for working in close proximity to humans. However, having this low stiffness creates challenges when operating in terms of control and sensitivity to disturbances. To alleviate this issue, soft robots often have built-in stiffness tuning mechanisms that allow for controlled increases in stiffness. Additionally, redundant pneumatic manipulators can utilize antagonistic pressure to achieve identical positions under increased stiffness. In this paper, we develop a model to predict the stiffness and configuration of a pneumatic soft manipulator under different pressure inputs and external forces. The model is developed based on the physical characteristics of a soft manipulator while enabling efficient parameter estimation and computation. The efficacy of the modeling approach is supported via experimental results.