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

Survey: Robot Programming by Demonstration

Robot PbD started about 30 years ago, growing importantly during the past decade. The rationale for moving from purely preprogrammed robots to very flexible user-based interfaces for training the robot to perform a task is three-fold. First and foremost, PbD, also referred to as {\em imitation learning} is a powerful mechanism for reducing the complexity of search spaces for learning. When observing either good or bad examples, one can reduce the search for a possible solution, by either starting the search from the observed good solution (local optima), or conversely, by eliminating from the search space what is known as a bad solution. Imitation learning is, thus, a powerful tool for enhancing and accelerating learning in both animals and artifacts. Second, imitation learning offers an implicit means of training a machine, such that explicit and tedious programming of a task by a human user can be minimized or eliminated (Figure \ref{fig:what-how}). Imitation learning is thus a ``natural'' means of interacting with a machine that would be accessible to lay people. And third, studying and modeling the coupling of perception and action, which is at the core of imitation learning, helps us to understand the mechanisms by which the self-organization of perception and action could arise during development. The reciprocal interaction of perception and action could explain how competence in motor control can be grounded in rich structure of perceptual variables, and vice versa, how the processes of perception can develop as means to create successful actions. PbD promises were thus multiple. On the one hand, one hoped that it would make the learning faster, in contrast to tedious reinforcement learning methods or trials-and-error learning. On the other hand, one expected that the methods, being user-friendly, would enhance the application of robots in human daily environments. Recent progresses in the field, which we review in this chapter, show that the field has make a leap forward the past decade toward these goals and that these promises may be fulfilled very soon

Human system modelling in support of manufacturing enterprise design and change

Organisations comprise human and technical systems that typically perform a variety of business, engineering and production roles. Human systems comprise individuals, people groups and teams that work systematically to conceive, implement, develop and manage the purposes of any enterprise in response to customer requirements. Recently attention has been paid to modelling aspects of people working within production systems, with a view to improving: production performance, effective resource allocation and optimum resource management. In the research reported, graphical and computer executable models of people have been conceived and used in support of human systems engineering. The approach taken has been to systematically decompose and represent processes so that elemental production and management activities can be modelled as explicit descriptions of roles that human systems can occupy as role holders. First of all, a preliminary modelling method (MM1) was proposed for modelling human systems in support of engineering enterprise; then MM1 was implemented and tested in a case study company 1. Based on findings of this exploratory research study an improved modelling method (MM2) was conceived and instrumented. Here characterising customer related product dynamic impacts extended MM1 modelling concepts and methods and related work system changes. MM2 was then tested in case study company 2 to observe dynamic behaviours of selected system models derived from actual company knowledge and data. Case study 2 findings enabled MM2 to be further improved leading to MM3. MM3 improvements stem from the incorporation of so-called DPU (Dynamic Producer Unit) concepts, related to the modelling of human and technical resource system components . Case study 4 models a human system for targeted users i.e. production managers etc to facilitate analysis of human configuration and also cost modelling. Modelling approaches MM2, MM3 and also Case Study 4 add to knowledge about ways of facilitating quantitative analysis and comparison between different human system configurations. These new modelling methods allow resource system behaviours to be matched to specific, explicitly defined, process-oriented requirements drawn from manufacturing workplaces currently operating in general engineering, commercial furniture and white goods industry sectors