Grounded spatial symbols for task planning based on experience

Abstract

Providing autonomous humanoid robots with the abilities to react in an adaptive and intelligent manner involves low level control and sensing as well as high level reasoning. However, the integration of both levels still remains challenging due to the representational gap between the continuous state space on the sensorimotor level and the discrete symbolic entities used in high level reasoning. In this work, we approach the problem of learning a representation of the space which is applicable on both levels. This representation is grounded on the sensorimotor level by means of exploration and on the language level by making use of common sense knowledge. We demonstrate how spatial knowledge can be extracted from these two sources of experience. Combining the resulting knowledge in a systematic way yields a solution to the grounding problem which has the potential to substantially decrease the learning effort.