Interpreting Language in Context in CommandTalk

This paper will focus on how two representations of context are used in CommandTalk to correctly interpret the user's spoken utterances: situational context represents the current state of the simulation, and linguistic context represents the history of the user's linguistic acts

A corpus-based analysis of route instructions in human-robot interaction

This paper investigates how users employ spatial descriptions to navigate a speech-enabled robot. We created a simulated environment in which users gave route instructions in a dialogic real-time interaction with a robot, which was operated by naïve participants. The ability of robot monitoring was also manipulated in two experimental conditions. The results provide evidence that the content of the instructions and strategies of the users vary depending on the conditions and demands of the interaction. As expected, the route instructions frequently were underspecified and arbitrary. The findings of this study elucidate the complexity in interpreting spatial language in HRI. However, they also point to the need for endowing mobile robots with richer dialogue resources to compensate for the uncertainties arising from language as well as the environment

Towards responsive Sensitive Artificial Listeners

This paper describes work in the recently started project SEMAINE, which aims to build a set of Sensitive Artificial Listeners – conversational agents designed to sustain an interaction with a human user despite limited verbal skills, through robust recognition and generation of non-verbal behaviour in real-time, both when the agent is speaking and listening. We report on data collection and on the design of a system architecture in view of real-time responsiveness

Crossmodal content binding in information-processing architectures

Operating in a physical context, an intelligent robot faces two fundamental problems. First, it needs to combine information from its different sensors to form a representation of the environment that is more complete than any of its sensors on its own could provide. Second, it needs to combine high-level representations (such as those for planning and dialogue) with its sensory information, to ensure that the interpretations of these symbolic representations are grounded in the situated context. Previous approaches to this problem have used techniques such as (low-level) information fusion, ontological reasoning, and (high-level) concept learning. This paper presents a framework in which these, and other approaches, can be combined to form a shared representation of the current state of the robot in relation to its environment and other agents. Preliminary results from an implemented system are presented to illustrate how the framework supports behaviours commonly required of an intelligent robot

Interactively Picking Real-World Objects with Unconstrained Spoken Language Instructions

Comprehension of spoken natural language is an essential component for robots to communicate with human effectively. However, handling unconstrained spoken instructions is challenging due to (1) complex structures including a wide variety of expressions used in spoken language and (2) inherent ambiguity in interpretation of human instructions. In this paper, we propose the first comprehensive system that can handle unconstrained spoken language and is able to effectively resolve ambiguity in spoken instructions. Specifically, we integrate deep-learning-based object detection together with natural language processing technologies to handle unconstrained spoken instructions, and propose a method for robots to resolve instruction ambiguity through dialogue. Through our experiments on both a simulated environment as well as a physical industrial robot arm, we demonstrate the ability of our system to understand natural instructions from human operators effectively, and how higher success rates of the object picking task can be achieved through an interactive clarification process.Comment: 9 pages. International Conference on Robotics and Automation (ICRA) 2018. Accompanying videos are available at the following links: https://youtu.be/_Uyv1XIUqhk (the system submitted to ICRA-2018) and http://youtu.be/DGJazkyw0Ws (with improvements after ICRA-2018 submission

Intentional processing as a key for rational behaviour through natural interaction

28 pages, 13 figures.-- Special Issue: "Symbiotic Performance between Humans and Intelligent Systems".This paper presents an interaction model pursuing flexible and coherent human–computer interaction. Starting from a cognitive architecture for Natural Interaction, an agent-based design is presented, focusing particularly on the role of the interaction agent. Regarding the intentional processing within this agent, the Threads Model is proposed. Finally, its implementation is described and evaluated to find out the integrity of the intentional approach.We thank the ISYS research group at the Technical University of Madrid and the LABDA research group at the Carlos III University of Madrid for their support during the design and development of the interaction agent, and everyone involved in the ADVICE (IST 1999-11305), VIP-ADVISOR (IST 2001-32440), and recent IntegraTV4All (FIT-350301-2004-2) projects for their hard work

Explorations in engagement for humans and robots

This paper explores the concept of engagement, the process by which individuals in an interaction start, maintain and end their perceived connection to one another. The paper reports on one aspect of engagement among human interactors--the effect of tracking faces during an interaction. It also describes the architecture of a robot that can participate in conversational, collaborative interactions with engagement gestures. Finally, the paper reports on findings of experiments with human participants who interacted with a robot when it either performed or did not perform engagement gestures. Results of the human-robot studies indicate that people become engaged with robots: they direct their attention to the robot more often in interactions where engagement gestures are present, and they find interactions more appropriate when engagement gestures are present than when they are not.Comment: 31 pages, 5 figures, 3 table