Natural language generation for social robotics: Opportunities and challenges

In the increasingly popular and diverse research area of social robotics, the primary goal is to develop robot agents that exhibit socially intelligent behaviour while interacting in a face-to-face context with human partners. An important aspect of face-to-face social conversation is fluent, flexible linguistic interaction: as Bavelas et al. [1] point out, face-to-face dialogue is both the basic form of human communication and the richest and most flexible, combining unrestricted verbal expression with meaningful non-verbal acts such as gestures and facial displays, along with instantaneous, continuous collaboration between the speaker and the listener. In practice, however, most developers of social robots tend not to use the full possibilities of the unrestricted verbal expression afforded by face-to-face conversation; instead, they generally tend to employ relatively simplistic processes for choosing the words for their robots to say. This contrasts with the work carried out Natural Language Generation (NLG), the field of computational linguistics devoted to the automated production of high-quality linguistic content: while this research area is also an active one, in general most effort in NLG is focussed on producing high-quality written text. This article summarises the state-of-the-art in the two individual research areas of social robotics and natural language generation. It then discusses the reasons why so few current social robots make use of more sophisticated generation techniques. Finally, an approach is proposed to bringing some aspects of NLG into social robotics, concentrating on techniques and tools that are most appropriate to the needs of socially interactive robots

Reference and the facilitation of search in spatial domains

This is a pre-final version of the article, whose official publication is expected in the winter of 2013-14.Peer reviewedPreprin

Report on the Second Second Challenge on Generating Instructions in Virtual Environments (GIVE-2.5)

GIVE-2.5 evaluates eight natural language generation (NLG) systems that guide human users through solving a task in a virtual environment. The data is collected via the Internet, and to date, 536 interactions of subjects with one of the NLG systems have been recorded. The systems are compared using both task performance measures and subjective ratings by human users

Asking for Help Using Inverse Semantics

Abstract—Robots inevitably fail, often without the ability to recover autonomously. We demonstrate an approach for enabling a robot to recover from failures by communicating its need for specific help to a human partner using natural language. Our approach automatically detects failures, then generates targeted spoken-language requests for help such as “Please give me the white table leg that is on the black table. ” Once the human partner has repaired the failure condition, the system resumes full autonomy. We present a novel inverse semantics algorithm for generating effective help requests. In contrast to forward semantic models that interpret natural language in terms of robot actions and perception, our inverse semantics algorithm generates requests by emulating the human’s ability to interpret a request using the Generalized Grounding Graph (G3) framework. To assess the effectiveness of our approach, we present a corpus-based online evaluation, as well as an end-to-end user study, demonstrating that our approach increases the effectiveness of human interventions compared to static requests for help. I