Editorial: Language and Robotics

Tadahiro Taniguchi, Takato Horii, Xavier Hinaut, Michael Spranger, Daichi Mochihashi, Takayuki Nagai

Abstract: Language in the real-world environment involves a wide range of challenges in robotics and artificial intelligence (AI). Service robots are required to communicate and collaborate with people using language in the real-world environment. When a robot receives a spoken command from a user in a domestic environment, the robot must understand its meaning in the context of the specific environment. For example, to understand the meaning of "please bring me a pen in Takato's room" the robot needs to know where to find a pen and where Takato's room is. Futhermore, words or expressions (i.e. sounds processed as symbols) can be invented naturally in our daily environment and their meaning can change [1] over time (i.e. depending on the culture or age of the speaker). Robots thus need to adapt like humans to these versatile aspects of language and demonstrate the ability to learn any language [2]. In robotics, language understanding inevitably involves multimodal learning, semantic mapping, and behavior learning. To enable a robot to interact orally with people in a long-term manner, we need to develop an AI that makes a robot learn and adapt to language in the real-world environment and in an on-line manner. This topic thus raises several challenges to bridge the gap from low-level sensorimotor interaction [3] to high-level compositional symbolic communication. Taking inspiration of how children acquire language can help to design the simplest mechanisms to deal with these challen...