When describing routes not in the current environment, a common strategy is to anchor the description in configurations of salient landmarks, complementing the verbal descriptions by "placing" the non-visible landmarks in the gesture space.  Understanding such multimodal descriptions and later locating the landmarks from real world is a challenging task for the hearer, who must interpret speech and gestures in parallel, fuse information from both modalities, build a mental representation of the description, and ground the knowledge to real world landmarks.  In this paper, we model the hearer's task, using a multimodal spatial description corpus we collected.  To reduce the variability of verbal descriptions, we simplified the setup to use simple objects as landmarks.  We describe a real-time system to  evaluate the separate and joint contribution of the modalities. We show that gestures not only help to improve the overall system performance, even if to a large extent they encode redundant information, but also result in earlier final correct interpretations. Being able to build and apply representations incrementally will be of use in more dialogical settings, we argue, where it can enable immediate clarification in cases of mismatch.

This issue of AI Magazine brings together a collection of articles on challenges, mechanisms, and research progress in turn-taking and coordination between humans and machines.

This issue of AI Magazine brings together a collection of articles on challenges, mechanisms, and research progress in turn-taking and coordination between humans and machines. The contributing authors work in interrelated fields of spoken dialog systems, intelligent virtual agents, human-computer interaction, human-robot interaction, and semiautonomous collaborative systems and explore core concepts in coordinating speech and actions with virtual agents, robots, and other autonomous systems. Several of the contributors participated in the AAAI Spring Symposium on Turn-Taking and Coordination in Human-Machine Interaction, held in March 2015, and several articles in this issue are extensions of work presented at that symposium. The articles in the collection address key modeling, methodological, and computational challenges in achieving effective coordination with machines, propose solutions that overcome these challenges under sensory, cognitive, and resource restrictions, and illustrate how such solutions can facilitate coordination across diverse and challenging domains. The contributions highlight turn-taking and coordination in human-machine interaction as an emerging and evolving research area with important implications for future applications of AI.

Side-participants (SPs) in multiparty dialogue establish and maintain their status as currently non-contributing, but integrated partners of the conversation by continuing to track, and be seen to be tracking, the conversation. To investigate strategies for realising such ‘active side-participant’ behaviour, we constructed an experimental setting where a humanoid robot appeared to track (overhear) a two-party conversation coming out of loudspeakers. We equipped the robot with ‘eyes’ (small displays) with movable pupils, to be able to separately control head-turning and gaze. Using information from the pre-processed conversations, we tested various strategies (random, reactive, predictive) for controlling gaze and head-turning. We asked human raters to judge videos of such tracking behaviour of the robot, and found that strategies making use of independent control of gaze and head direction were significantly preferred. Moreover, the ‘sensible’ strategies (reactive, predictive) were reliably distinguished from the baseline (random turning).We take this as indication that gaze is an important, semi-independent modality, and that our paradigm of off-line evaluation of overhearer behaviour using recorded interactions is a promising one for costeffective study of more sophisticated tracking models, and can stand as a proxy for testing models of actual side-participants (whose presence would be known, and would influence, the conversation they are part of).

The RoboCup@Home league has been founded in 2006with the idea to drive research in AI and related fieldstowards autonomous and interactive robots that copewith real life tasks in supporting humans in everday life.The yearly competition format establishes benchmarkingas a continuous process with yearly changes insteadof a single challenge. We discuss the current state andfuture perspectives of this endeavor.

In this paper, we present a hybrid grammar formalism designed to learn structured models of natural iconic gesture performances that allow for compressed representation and robust recognition. We analyze a dataset of iconic gestures and show how the proposed Feature-based Stochastic Context-Free Grammar (FSCFG) can generalize over both structural and feature-based variations among different gesture performances.

Modeling interaction with robots raises new and different challenges for dialog modeling than traditional dialog modeling with less embodied machines. We present four case studies of implementing a typical human-robot interaction scenario with different state-of-the-art dialog frameworks in order to identify challenges and pitfalls specific to HRI and potential solutions. The results are discussed with a special focus on the interplay between dialog and task modeling on robots.

Modeling interaction with robots raises new and different challenges for dialog modeling than traditional dialog modeling with less embodied machines. We present four case studies of implementing a typical human-robot interaction scenario with different state-of-the-art dialog frameworks in order to identify challenges and pitfalls specific to HRI and potential solutions. The results are discussed with a special focus on the interplay between dialog and task modeling on robots.

While current techniques for human-robot interaction modeling are typically limited to restrictive command-control style, traditional dialog modeling approaches are not directly applicable to robotics due to the lack of real-world integration. We present an approach that combines insights from dialog modeling with software-engineering demands that arise in robotics system research to provide a generalizable framework that can easily be applied to new scenarios. This goal is achieved by defining interaction patterns that combine abstract task states (such as task accepted or failed) with robot dialog acts (such as assertion or apology). An evaluation of the usability for robotic experts and novices showed that both groups were able to program 3 out of 5 dialog patterns in one hour while showing a steep learning curve. We argue that the proposed approach allows for less restricted and more informative human-robot interactions.