  Towards addressing the Symbol Grounding Problem and motivated by early
childhood language development, we leverage a robot which has been equipped
with an approximate model of curiosity with particular focus on bottom-up
building of unsupervised categories grounded in the physical world. That is,
rather than starting with a top-down symbol (e.g., a word referring to an
object) and providing meaning through the application of predetermined samples,
the robot autonomously and gradually breaks up its exploration space into a
series of increasingly specific unlabeled categories at which point an external
expert may optionally provide a symbol association. We extend prior work by
using a robot that can observe the visual world, introducing a higher
dimensional sensory space, and using a more generalizable method of category
building. Our experiments show that the robot learns categories based on
actions and what it visually observes, and that those categories can be
symbolically grounded into.https://info.arxiv.org/help/prep#comments
