  We propose a K-Means based prediction system, nicknamed SERVANT (Scene
Recognition Through Visual and Acoustic Cues), that is capable of recognizing
environmental scenes through analysis of ambient sound and color cues. The
concept and implementation originated within the Learning branch of the
Intelligent Wearable Robotics Project (also known as the Third Arm project) at
the Stanford Artificial Intelligence Lab-Toyota Center (SAIL-TC). The Third Arm
Project focuses on the development and conceptualization of a robotic arm that
can aid users in a whole array of situations: i.e. carrying a cup of coffee,
holding a flashlight. Servant uses a K-Means fit-and-predict architecture to
classify environmental scenes, such as that of a coffee shop or a basketball
gym, using visual and auditory cues. Following such classification, Servant can
recommend contextual actions based on prior training.
