Validation of Human Motor Development Scales with Humanoid Robots

We investigate how humanoid robots can learn a series of motor skills defined by human infant motor development scales, and we evaluate which scales are comprehensive enough to serve as self-contained guides for developing gross motor skills up to standing and walking. We represent each motor development scale as a developmental graph that guides the training of a humanoid robot. We then apply this graph within a reinforcement learning framework so that the robot learns skills following the natural phases of human motor development described by the scale. Our work demonstrates that graphs derived from three out of four popular pediatric motor scales can successfully guide humanoid robots to develop these skills. The fourth scale misses some intermediate milestones that the other scales have, which causes some failures. After adding a missing milestone in its graph, it also enables the robot to develop those skills successfully.