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Mobility is a causal factor in development. Children with mobility impairments may rely upon power mobility for independence and thus require advanced driving skills to function independently. Our previous studies show that while infants can learn to drive directly to a goal using conventional joysticks in several months of training, they are unable in this timeframe to acquire the advanced skill to avoid obstacles while driving. Without adequate driving training, children are unable to explore the environment safely, the consequences of which may in turn increase their risk for developmental delay. The goal of this research therefore is to train children seated on mobile robots to purposefully and safely drive indoors. In this paper, we present results where ten typically-developing toddlers are trained to drive a robot within an obstacle course. We also report a case study with a toddler with spina-bifida who cannot independently walk. Using algorithms based on artificial potential fields to avoid obstacles, we create force field on the joystick that trains the children to navigate while avoiding obstacles. In this “assist-as-needed” approach, if the child steers the joystick outside a force tunnel centered on the desired direction, the driver experiences a bias force on the hand. Our results suggest that the use of a force-feedback joystick may yield faster learning than the use of a conventional joystick.