Leolani: a reference machine with a theory of mind for social communication
This work addresses social communication for robotics, but it appears incremental as it builds on existing theory of mind principles without claiming major breakthroughs.
The authors tackled the problem of social communication in robots by developing Leolani, a robot that models relativity of knowledge and perception using theory of mind, resulting in a system that learns from interactions to resolve uncertainties and share awareness.
Our state of mind is based on experiences and what other people tell us. This may result in conflicting information, uncertainty, and alternative facts. We present a robot that models relativity of knowledge and perception within social interaction following principles of the theory of mind. We utilized vision and speech capabilities on a Pepper robot to build an interaction model that stores the interpretations of perceptions and conversations in combination with provenance on its sources. The robot learns directly from what people tell it, possibly in relation to its perception. We demonstrate how the robot's communication is driven by hunger to acquire more knowledge from and on people and objects, to resolve uncertainties and conflicts, and to share awareness of the per- ceived environment. Likewise, the robot can make reference to the world and its knowledge about the world and the encounters with people that yielded this knowledge.