Interaction and Learning in a Humanoid Robot Magic Performance

Magicians have been a source of entertainment for many centuries, with the ability to play on human bias, and perception to create an entertaining experience. There has been rapid growth in robotics throughout industrial applications; where primary challenges include improving human-robot interaction, and robotic perception. Despite preliminary work in expressive AI, which aims to use AI for entertainment; there has not been direct application of fully embodied autonomous agents (vision, speech, learning, planning) to entertainment domains. This paper describes preliminary work towards the use of magic tricks as a method for developing fullyembodied autonomous agents. A card trick is developed requiring vision, communication, interaction, and learning capabilities all of which are coordinated using our script representation. Our work is evaluated quantitatively through experimentation, and qualitatively through aquiring 2nd place at the 2016 IROS Humanoid Application Challenge. A video of the live performance can be found at https://youtu.be/OMpcmcPWAVM.