Online touch behavior recognition of hard-cover robot using temporal decision tree classifier

Touch is obviously an important channel along with vision and speech for natural human robot Interaction. However, as most service robots are generally specialized for their own service, touch-centered shape design and additional costs/computation less related to their own tasks can represent a limit to the application of a touch system on a service robot. This paper originated from the motivation to apply a touch system with lower costs/computation to robots without design modifications. The proposed touch recognition system features hardware that is simply composed of charge-transfer touch sensor arrays, an accelerometer and a temporal decision tree classifier intended for online recognition and computational time reduction. Experiments performed by 12 people shows the practicability of the system. The results showed an average recognition rate of 83% with respect to the 4 touch patterns of hit, beat, rub and push.