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Objective: The purpose of this report is to provide insight from pediatric stakeholders with a shared desire to facilitate a revision of the current United States regulatory pathways for the development of pediatric healthcare devices., Methods: On August 5, 2020, a group of innovators, engineers, professors and clinicians met to discuss challenges and opportunities for the development of new medical devices for pediatric health and the importance of creating a regulatory environment that encourages and accelerates the research and development of such devices. On January 6, 2021, this group joined regulatory experts at a follow-up meeting., Results: One of the primary issues identified was the need to present decision-makers with opportunities that change the return-on-investment balance between adult and pediatric devices to promote investment in pediatric devices., Discussion/conclusion: Several proposed strategies were discussed, and these strategies can be divided into two broad categories: 1. Removal of real and perceived barriers to pediatric device innovation; 2. Increasing incentives for pediatric device innovation.

Medical students are attracted by the prospect of a meaningful addition to their clinical work. Engineering students are excited by a unique opportunity to learn directly alongside their medical student peers. For both, as well as the scientific community at large, the boutique program at the University of Southern California (USC) linking engineering and medical training at the graduate level is instructive of a new way of approaching engineering education that can potentially provide benefits to both students and society. Students who have grown up in an era of ?mass customization? in the retail and service industries can enjoy that same degree of flexibility also in the realm of education. At the same time, society gains engineers who have developed an increased empathy and awareness of the clinical contexts in which their innovations will be implemented.

In this paper, optimal unsupervised motor learning is defined to be a technique for finding the coordinate system of minimum dimensionality which can adequately describe a particular motor task. An explicit method is provided for learning a stable controller that translates commands within the new coordinate system into motor variables appropriate for plant control. The method makes use of previously described neural network algorithms including the generalized Hebbian algorithm, basis-function trees, and trajectory extension learning. Examples of applications to a real direct-drive two joint planar robot arm and a simulated three joint robot arm with visual sensing are given.

Nonlinear function approximation is often solved by finding a set of coefficients for a finite number of fixed nonlinear basis functions. However, if the input data are drawn from a high-dimensional space, the number of required basis functions grows exponentially with dimension, leading many to suggest the use of adaptive nonlinear basis functions whose parameters can be determined by iterative methods. The author proposes a technique based on the idea that for most of the data, only a few dimensions of the input may be necessary to compute the desired output function. Additional input dimensions are incorporated only where needed. The learning procedure grows a tree whose structure depends upon the input data and the function to be approximated. This technique has a fast learning algorithm with no local minima once the network shape is fixed, and it can be used to reduce the number of required measurements in situations where there is a cost associated with sensing. Three examples are given: controlling the dynamics of a simulated planar two-joint robot arm, predicting the dynamics of the chaotic Mackey-Glass equation, and predicting pixel values in real images from pixel values above and to the left.