Robot learning using Symbol Grounding

Solving the Symbol Grounding Problem (SGP) is crucial for the autonomous robot learning. The failure of classical AI in delivering smart robots has forced many researchers to address the problem earnestly. In this paper we present a methodology to solve this problem by facilitating a human instructor to interact with a robot using a Microsoft Kinect™ sensor so as to ground symbols. The instructor exhibits the physical objects and also provides the corresponding symbols using voice commands to a robot. The robot captures the physical attributes of different objects for the symbol to elaborate its own semantics autonomously and grounds the same within. Grounding is achieved by toiling on sensory information to acquire the commonalities from the attributes while symbol theft enables the grounding of meta-level symbols or aliases. Apart from the formal description of the inherent grounding mechanism, the paper also discusses the results obtained from actual experiments that eventually make the robot identify and point to the concerned physical objects.