Lilia, A Showcase for Fast Bootstrap of Conversation-Like Dialogues Based on a Goal-Oriented System

International audienceRecently many works have proposed to cast human-machine interaction in a sentence generation scheme. Neural networks models can learn how to generate a probable sentence based on the user's statement along with a partial view of the dialogue history. While appealing to some extent, these approaches require huge training sets of general-purpose data and lack a principled way to intertwine language generation with information retrieval from back-end resources to fuel the dialogue with actualised and precise knowledge. As a practical alternative, in this paper, we present Lilia, a showcase for fast bootstrap of conversation-like dialogues based on a goal-oriented system. First, a comparison of goal-oriented and conversational system features is led, then a conversion process is described for the fast bootstrap of a new system, finalised with an on-line training of the system's main components. Lilia is dedicated to a chitchat task, where speakers exchange viewpoints on a displayed image while trying collaboratively to derive its author's intention. Evaluations with user trials showed its efficiency in a realistic setup

A Distributed Tactile Sensor for Intuitive Human-Robot Interfacing

Safety of human-robot physical interaction is enabled not only by suitable robot control strategies but also by suitable sensing technologies. For example, if distributed tactile sensors were available on the robot, they could be used not only to detect unintentional collisions, but also as human-machine interface by enabling a new mode of social interaction with the machine. Starting from their previous works, the authors developed a conformable distributed tactile sensor that can be easily conformed to the different parts of the robot body. Its ability to estimate contact force components and to provide a tactile map with an accurate spatial resolution enables the robot to handle both unintentional collisions in safe human-robot collaboration tasks and intentional touches where the sensor is used as human-machine interface. In this paper, the authors present the characterization of the proposed tactile sensor and they show how it can be also exploited to recognize haptic tactile gestures, by tailoring recognition algorithms, well known in the image processing field, to the case of tactile images. In particular, a set of haptic gestures has been defined to test three recognition algorithms on a group of 20 users. The paper demonstrates how the same sensor originally designed to manage unintentional collisions can be successfully used also as human-machine interface

Flexible Task Execution and Cognitive Control in Human-Robot Interaction

A robotic system that interacts with humans is expected to flexibly execute structured cooperative tasks while reacting to unexpected events and behaviors. In this thesis, these issues are faced presenting a framework that integrates cognitive control, executive attention, structured task execution and learning. In the proposed approach, the execution of structured tasks is guided by top-down (task-oriented) and bottom-up (stimuli-driven) attentional processes that affect behavior selection and activation, while resolving conflicts and decisional impasses. Specifically, attention is here deployed to stimulate the activations of multiple hierarchical behaviors orienting them towards the execution of finalized and interactive activities. On the other hand, this framework allows a human to indirectly and smoothly influence the robotic task execution exploiting attention manipulation. We provide an overview of the overall system architecture discussing the framework at work in different applicative contexts. In particular, we show that multiple concurrent tasks/plans can be effectively orchestrated and interleaved in a flexible manner; moreover, in a human-robot interaction setting, we test and assess the effectiveness of attention manipulation and learning processes

A survey of technologies supporting design of a multimodal interactive robot for military communication

Purpose – This paper presents a survey of research into interactive robotic systems for the purpose of identifying the state of the art capabilities as well as the extant gaps in this emerging field. Communication is multimodal. Multimodality is a representation of many modes chosen from rhetorical aspects for its communication potentials. The author seeks to define the available automation capabilities in communication using multimodalities that will support a proposed Interactive Robot System (IRS) as an AI mounted robotic platform to advance the speed and quality of military operational and tactical decision making. Design/methodology/approach – This review will begin by presenting key developments in the robotic interaction field with the objective of identifying essential technological developments that set conditions for robotic platforms to function autonomously. After surveying the key aspects in Human Robot Interaction (HRI), Unmanned Autonomous System (UAS), visualization, Virtual Environment (VE) and prediction, the paper then proceeds to describe the gaps in the application areas that will require extension and integration to enable the prototyping of the IRS. A brief examination of other work in HRI-related fields concludes with a recapitulation of the IRS challenge that will set conditions for future success. Findings – Using insights from a balanced cross section of sources from the government, academic, and commercial entities that contribute to HRI a multimodal IRS in military communication is introduced. Multimodal IRS (MIRS) in military communication has yet to be deployed. Research limitations/implications – Multimodal robotic interface for the MIRS is an interdisciplinary endeavour. This is not realistic that one can comprehend all expert and related knowledge and skills to design and develop such multimodal interactive robotic interface. In this brief preliminary survey, the author has discussed extant AI, robotics, NLP, CV, VDM, and VE applications that is directly related to multimodal interaction. Each mode of this multimodal communication is an active research area. Multimodal human/military robot communication is the ultimate goal of this research. Practical implications – A multimodal autonomous robot in military communication using speech, images, gestures, VST and VE has yet to be deployed. Autonomous multimodal communication is expected to open wider possibilities for all armed forces. Given the density of the land domain, the army is in a position to exploit the opportunities for human–machine teaming (HMT) exposure. Naval and air forces will adopt platform specific suites for specially selected operators to integrate with and leverage this emerging technology. The possession of a flexible communications means that readily adapts to virtual training will enhance planning and mission rehearsals tremendously. Social implications – Interaction, perception, cognition and visualization based multimodal communication system is yet missing. Options to communicate, express and convey information in HMT setting with multiple options, suggestions and recommendations will certainly enhance military communication, strength, engagement, security, cognition, perception as well as the ability to act confidently for a successful mission. Originality/value – The objective is to develop a multimodal autonomous interactive robot for military communications. This survey reports the state of the art, what exists and what is missing, what can be done and possibilities of extension that support the military in maintaining effective communication using multimodalities. There are some separate ongoing progresses, such as in machine-enabled speech, image recognition, tracking, visualizations for situational awareness, and virtual environments. At this time, there is no integrated approach for multimodal human robot interaction that proposes a flexible and agile communication. The report briefly introduces the research proposal about multimodal interactive robot in military communication

Augmented Reality to Facilitate a Conceptual Understanding of Statics in Vocational Education

At the core of the contribution of this dissertation there is an augmented reality (AR) environment, StaticAR, that supports the process of learning the fundamentals of statics in vocational classrooms, particularly in carpentry ones. Vocational apprentices are expected to develop an intuition of these topics rather than a formal comprehension. We have explored the potentials of the AR technology for this pedagogical challenge. Furthermore, we have investigated the role of physical objects in mixed-reality systems when they are implemented as tangible user interfaces (TUIs) or when they serve as a background for the augmentation in handheld AR. This thesis includes four studies. In the first study, we used eye-tracking methods to look for evidences of the benefits associated to TUIs in the learning context. We designed a 3D modelling task and compared users' performance when they completed it using a TUI or a GUI. The gaze measures that we analysed further confirmed the positive impact that TUIs can have on the learners' experience and enforced the empirical basis for their adoption in learning applications. The second study evaluated whether the physical interaction with models of carpentry structures could lead to a better understanding of statics principles. Apprentices engaged in a learning activity in which they could manipulate physical models that were mechanically augmented, allowing for exploring how structures react to external loads. The analysis of apprentices' performance and their gaze behaviors highlighted the absence of clear advantages in exploring statics through manipulation. This study also showed that the manipulation might prevent students from noticing aspects relevant for solving statics problems. From the second study we obtained guidelines to design StaticAR which implements the magic-lens metaphor: a tablet augments a small-scale structure with information about its structural behavior. The structure is only a background for the augmentation and its manipulation does not trigger any function, so in the third study we asked to what extent it was important to have it. We rephrased this question to whether users would look directly at the structure instead of seeing it only through a tablet. Our findings suggested that a shift of attention from the screen to the physical object (a structure in our case) might occur in order to sustain users' spatial orientation when they change positions. In addition, the properties of the gaze shift (e.g. duration) could depend on the features of the task (e.g. difficulty) and of the setup (e.g. stability of the augmentation). The focus of our last study was the digital representation of the forces that act in a loaded structure. From the second study we observed that the physical manipulation failed to help apprentices understanding the way the forces interact with each other. To overcome this issue, our solution was to combine an intuitive representation (springs) with a slightly more formal one (arrows) which would show both the nature of the forces and the interaction between them. In this study apprentices used the two representations to collaboratively solve statics problems. Even though apprentices had difficulties in interpreting the two representations, there were cases in which they gained a correct intuition of statics principles from them. In this thesis, besides describing the designed system and the studies, implications for future directions are discussed

Decisional issues during human-robot joint action

In the future, robots will become our companions and co-workers. They will gradually appear in our environment, to help elderly or disabled people or to perform repetitive or unsafe tasks. However, we are still far from a real autonomous robot, which would be able to act in a natural, efficient and secure manner with humans. To endow robots with the capacity to act naturally with human, it is important to study, first, how humans act together. Consequently, this manuscript starts with a state of the art on joint action in psychology and philosophy before presenting the implementation of the principles gained from this study to human-robot joint action. We will then describe the supervision module for human-robot interaction developed during the thesis. Part of the work presented in this manuscript concerns the management of what we call a shared plan. Here, a shared plan is a a partially ordered set of actions to be performed by humans and/or the robot for the purpose of achieving a given goal. First, we present how the robot estimates the beliefs of its humans partners concerning the shared plan (called mental states) and how it takes these mental states into account during shared plan execution. It allows it to be able to communicate in a clever way about the potential divergent beliefs between the robot and the humans knowledge. Second, we present the abstraction of the shared plans and the postponing of some decisions. Indeed, in previous works, the robot took all decisions at planning time (who should perform which action, which object to use…) which could be perceived as unnatural by the human during execution as it imposes a solution preferentially to any other. This work allows us to endow the robot with the capacity to identify which decisions can be postponed to execution time and to take the right decision according to the human behavior in order to get a fluent and natural robot behavior. The complete system of shared plans management has been evaluated in simulation and with real robots in the context of a user study. Thereafter, we present our work concerning the non-verbal communication needed for human-robot joint action. This work is here focused on how to manage the robot head, which allows to transmit information concerning what the robot's activity and what it understands of the human actions, as well as coordination signals. Finally, we present how to mix planning and learning in order to allow the robot to be more efficient during its decision process. The idea, inspired from neuroscience studies, is to limit the use of planning (which is adapted to the human-aware context but costly) by letting the learning module made the choices when the robot is in a "known" situation. The first obtained results demonstrate the potential interest of the proposed solution

A dialogue system for multimodal human-robot interaction

This paper presents a POMDP-based dialogue system for multimodal human-robot interaction (HRI). Our aim is to exploit a dialogical paradigm to allow a natural and robust interaction between the human and the robot. The proposed dialogue system should improve the robustness and the flexibility of the overall interactive system, including multimodal fusion, interpretation, and decision-making. The dialogue is represented as a Partially Observable Markov Decision Process (POMDPs) to cast the inherent communication ambiguity and noise into the dialogue model. POMDPs have been used in spoken dialogue systems, mainly for tourist information services, but their application to multimodal human-robot interaction is novel. This paper presents the proposed model for dialogue representation and the methodology used to compute a dialogue strategy. The whole architecture has been integrated on a mobile robot platform and has bee n tested in a human-robot interaction scenario to assess the overall performances with respect to baseline controllers. © 2013 ACM