Socially embedded learning of the office-conversant mobile robot Jijo-2

This paper explores a newly developing direction of machine learning called ''socially embedded learning". In this research we have been building an office-conversant mobile robot which autonomously moves around in an office environment, actively gathers information through close interaction with this environment including sensing multi-modal data and making dialog with people in the office, and acquires knowledge about the environment with which it ultimately becomes conversant. Here our major concerns are in how the close interaction between the learning system and its social environment can help or accelerate the systems learning process, and what kinds of prepared mechanisms are necessary for the emergence of such interactions. The office-conversant robot is a platform on which we implement our ideas and test their feasibility in a real-world setting. An overview of the system is given and two examples of implemented ideas, i.e. dialog-based map acquisition and route acquisition by following, are described in detail.