H uman-centered design thinking traditionally has been used in industry to create products; it is now being considered a powerful tool in health care to solve care delivery problems, educate staff members, and improve the patient experience. 1 Understanding the applications of human-centered design thinking can foster innovation by moving design from a convergent approach in which the best option is chosen from the existing alternatives toward a more divergent approach in which individuals create new alternatives. This method can produce innovations in health care and safer environments for staff members and patients. WHAT IS DESIGN THINKING?The phrase design thinking was coined by Tim Brown in 2008 and was primarily applied to business, with the goal to change consumer trends and behaviors while being fiscally conservative. 1 Tim Brown and Roger Martin led the Innovation, Design Engineering Organization; Brown developed this model to drive the growth of business. 2 Companies such as Apple use the design-thinking approach to create new and innovative products for consumers. 3 It is important to understand the fundamentals of design thinking to better appreciate its application to health care.Design thinking is a process of creating solutions to complex problems for specific user groups. The process focuses on needfinding, understanding, creating, thinking, and doing. 4 Needfinding is simply talking to people and discovering what their needs are to gain meaningful insights. The steps or elements of design thinking include empathizing, defining, ideating, prototyping, and testing. 4 By empathizing with the http://dx.

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We present a novel approach to path planning for robotic manipulators, in which paths are produced via iterative optimisation in the latent space of a generative model of robot poses. Constraints are incorporated through the use of constraint satisfaction classifiers operating on the same space. Optimisation leverages gradients through our learned models that provide a simple way to combine goal reaching objectives with constraint satisfaction, even in the presence of otherwise non-differentiable constraints. Our models are trained in a task-agnostic manner on randomly sampled robot poses. In baseline comparisons against a number of widely used planners, we achieve commensurate performance in terms of task success, planning time and path length, performing successful path planning with obstacle avoidance on a real 7-DoF robot arm.