Showing papers by "Daniele Nardi published in 2013"

Learning environmental knowledge from task-based human-robot dialog

Abstract: This paper presents an approach for learning environmental knowledge from task-based human-robot dialog. Previous approaches to dialog use domain knowledge to constrain the types of language people are likely to use. In contrast, by introducing a joint probabilistic model over speech, the resulting semantic parse and the mapping from each element of the parse to a physical entity in the building (e.g., grounding), our approach is flexible to the ways that untrained people interact with robots, is robust to speech to text errors and is able to learn referring expressions for physical locations in a map (e.g., to create a semantic map). Our approach has been evaluated by having untrained people interact with a service robot. Starting with an empty semantic map, our approach is able ask 50% fewer questions than a baseline approach, thereby enabling more effective and intuitive human robot dialog.

Knowledge acquisition through human---robot multimodal interaction

Abstract: The limited understanding of the surrounding environment still restricts the capabilities of robotic systems in real world applications. Specifically, the acquisition of knowledge about the environment typically relies only on perception, which requires intensive ad hoc training and is not sufficiently reliable in a general setting. In this paper, we aim at integrating new acquisition devices, such as tangible user interfaces, speech technologies and vision-based systems, with established AI methodologies, to present a novel and effective knowledge acquisition approach. A natural interaction paradigm is presented, where humans move within the environment with the robot and easily acquire information by selecting relevant spots, objects, or other relevant landmarks. The synergy between novel interaction technologies and semantic knowledge leverages humans' cognitive skills to support robots in acquiring and grounding knowledge about the environment; such richer representation can be exploited in the realization of robot autonomous skills for task accomplishment.

On-line semantic mapping

Abstract: Human Robot Interaction is a key enabling feature to support the introduction of robots in everyday environments. However, robots are currently incapable of building representations of the environments that allow both for the execution of complex tasks and for an easy interaction with the user requesting them. In this paper, we focus on semantic mapping, namely the problem of building a representation of the environment that combines metric and symbolic information about the elements of the environment and the objects therein. Specifically, we extend previous approaches, by enabling on-line semantic mapping, that permits to add to the representation elements acquired through a long term interaction with the user. The proposed approach has been experimentally validated on different kinds of environments, several users, and multiple robotic platforms.

Kernel-Based Discriminative Re-ranking for Spoken Command Understanding in HRI

Abstract: Speech recognition is being addressed as one of the key technologies for a natural interaction with robots, that are targeting in the consumer market However, speech recognition in human-robot interaction is typically affected by noisy conditions of the operational environment, that impact on the performance of the recognition of spoken commands Consequently, finite-state grammars or statistical language models even though they can be tailored to the target domain exhibit high rate of false positives or low accuracy In this paper, a discriminative re-ranking method is applied to a simple speech and language processing cascade, based on off-the-shelf components in realistic conditions Tree kernels are here applied to improve the accuracy of the recognition process by re-ranking the n-best list returned by the speech recognition component The rationale behind our approach is to reduce the effort for devising domain dependent solutions in the design of speech interfaces for language processing in human-robot interactions

UNITOR-HMM-TK: Structured Kernel-based learning for Spatial Role Labeling

Abstract: In this paper the UNITOR-HMM-TK system participating in the Spatial Role Labeling task at SemEval 2013 is presented. The spatial roles classification is addressed as a sequence-based word classification problem: the SVM learning algorithm is applied, based on a simple feature modeling and a robust lexical generalization achieved through a Distributional Model of Lexical Semantics. In the identification of spatial relations, roles are combined to generate candidate relations, later verified by a SVM classifier. The Smoothed Partial Tree Kernel is applied, i.e. a convolution kernel that enhances both syntactic and lexical properties of the examples, avoiding the need of a manual feature engineering phase. Finally, results on three of the five tasks of the challenge are reported.

Human-Robot Collaboration for Semantic Labeling of the Environment

Abstract: Today’s robots are able to perform more and more complex tasks, which usually require a high degree of interaction with the environment they have to operate in. As a consequence, robotic systems should have a deep and specific knowledge of their workspaces, which goes far beyond a simple metric representation a robotic system can build up through SLAM (Simultaneous Localization and Mapping). In this paper, we present a novel human-robot collaboration approach, designed to extract 3D shapes associated to objects of interest pointed out by a human operator. The information regarding the segmented objects are then integrated into a metric map, built by the robot, providing a high-level representation of the environment that embodies all the knowledge required by a robot to actually execute complex tasks.

Knowledge Representation for Robots through Human-Robot Interaction

Abstract: The representation of the knowledge needed by a robot to perform complex tasks is restricted by the limitations of perception. One possible way of overcoming this situation and designing "knowledgeable" robots is to rely on the interaction with the user. We propose a multi-modal interaction framework that allows to effectively acquire knowledge about the environment where the robot operates. In particular, in this paper we present a rich representation framework that can be automatically built from the metric map annotated with the indications provided by the user. Such a representation, allows then the robot to ground complex referential expressions for motion commands and to devise topological navigation plans to achieve the target locations.

Knowledgeable talking robots

Abstract: Speech technologies nowadays available on mobile devices show an increased performance both in terms of the language that they are able to capture and in terms of reliability. The availability of performant speech recognition engines suggests the deployment of vocal interfaces also in consumer robots. In this paper, we report on our current work, by specifically focussing on the difficulties that arise in grounding the user's utterances in the environment where the robot is operating.

Ground Truth Acquisition of Humanoid Soccer Robot Behaviour

Abstract: In this paper an open source software for monitoring humanoid soccer robot behaviours is presented. The software is part of an easy to set up system, conceived for registering ground truth data that can be used for evaluating and testing methods such as robot coordination and localization. The hardware architecture of the system is designed for using multiple low-cost visual sensors (four Kinects). The software includes a foreground computation module and a detection unit for both players and ball. A graphical user interface has been developed in order to facilitate the creation of a shared multi-camera plan view, in which the observations of players and ball are re-projected to obtain global positions. The effectiveness of the implemented system has been proven using a laser sensor to measure the exact position of the objects of interest in the field.

Development of intelligent service robots

Abstract: The creation of intelligent robots has been a major goal of Artificial Intelligence since the early days and has provided many motivations to Artificial Intelligence researchers. Therefore, a large body of research has been done in this field and many relevantresultshaveshownthatintegrationofArtificialIntelligenceandRoboticstechniquesisaviableapproachtowardsthisgoal. ThisarticlesummarizestheeffortsandtheachievementsofseveralItalianresearchgroupsinthedevelopmentofintelligentrobotic systems characterized by a suitable integration of Artificial Intelligence and Robotic techniques. The contributions collected in this article show the long history of this research stream, the impact of the developed approaches in the scientific community, and the efforts towards actual deployment of the developed systems.

Integrated Balance Control on Uneven Terrain

Abstract: To reach competent Human-Robot Interaction, robots should be able to behave stably on uneven terrain in domestic environments. This paper addresses a technique, which integrates four balance control strategies and is used on Nao robot to realize walking on uneven terrain that is not modelled in advance. The most important two strategies are “Closed Loop Gait Pattern Generator” and “Posture Control”. The former one uses the filtered robot state based on Kalman filter. It helps to improve joint tracking, which is important for model based approaches. The latter one helps to make the trunk vertical to the ground. This strategy is very effective when walking on a slope. The other two strategies are “CoG (Center of Gravity) Height Control” and “Ankle Joint Control”, which are used to resist relatively large tilt and prevent potential falling over motion. abstract environment.