Grounded language in robotics

The symbol grounding problem and especially the detail of grounded language is a very new subject in computer science. It needs to be explained because its the key element of artificial intelligence. As an introductory example a warehouse robot has stored a json file:

{
  "knowledge": "pick up the red box in kitchen room",
  "information": [box, obstacle, entrance, room, pickup, drop, red, blue, yellow, kitchen, corridor, dining room],
  "data": [pos=(40,10), rgb_color=(100,70,20), dist=20, trajectory=traj3, direction=(40,10), battery=0.7],
}

This json file shows 3 of 4 layers from a DIKW pyramid. It is the current situation of the robot also known as the game state. According to the DIKW pyramid, this current situation is stored in different layers which have a different abstraction. The lowest data layer stores numerical information gathered from hardware sensors. While the information layer stores the vocabulary and the knowledge layer stores a concrete instruction.

The main task of the robots artificial intelligence is to translate between these layers. The human enters a command and the robot understands the command because its translated into the low level data layer. This translation process is called grounded language.

In contrast to former natural language processing (NLP) the goal is not to check the grammar of the input instruction e.g. to verify that the word "room" is a noun or that "red" is an adjective. So the question is not what language is by itself, but the problem has to do with converting from high level abstraction towards low level abstraction in the DIKW layers.