24,830 research outputs found
Conceptual spatial representations for indoor mobile robots
We present an approach for creating conceptual representations of human-made indoor environments using mobile
robots. The concepts refer to spatial and functional properties of typical indoor environments. Following ļ¬ndings
in cognitive psychology, our model is composed of layers representing maps at diļ¬erent levels of abstraction. The
complete system is integrated in a mobile robot endowed with laser and vision sensors for place and object recognition.
The system also incorporates a linguistic framework that actively supports the map acquisition process, and which
is used for situated dialogue. Finally, we discuss the capabilities of the integrated system
Robust Spoken Language Understanding for House Service Robots
Service robotics has been growing significantly in thelast years, leading to several research results and to a numberof consumer products. One of the essential features of theserobotic platforms is represented by the ability of interactingwith users through natural language. Spoken commands canbe processed by a Spoken Language Understanding chain, inorder to obtain the desired behavior of the robot. The entrypoint of such a process is represented by an Automatic SpeechRecognition (ASR) module, that provides a list of transcriptionsfor a given spoken utterance. Although several well-performingASR engines are available off-the-shelf, they operate in a generalpurpose setting. Hence, they may be not well suited in therecognition of utterances given to robots in specific domains. Inthis work, we propose a practical yet robust strategy to re-ranklists of transcriptions. This approach improves the quality of ASRsystems in situated scenarios, i.e., the transcription of roboticcommands. The proposed method relies upon evidences derivedby a semantic grammar with semantic actions, designed tomodel typical commands expressed in scenarios that are specificto human service robotics. The outcomes obtained throughan experimental evaluation show that the approach is able toeffectively outperform the ASR baseline, obtained by selectingthe first transcription suggested by the AS
A Review of Verbal and Non-Verbal Human-Robot Interactive Communication
In this paper, an overview of human-robot interactive communication is
presented, covering verbal as well as non-verbal aspects of human-robot
interaction. Following a historical introduction, and motivation towards fluid
human-robot communication, ten desiderata are proposed, which provide an
organizational axis both of recent as well as of future research on human-robot
communication. Then, the ten desiderata are examined in detail, culminating to
a unifying discussion, and a forward-looking conclusion
Exploring miscommunication and collaborative behaviour in human-robot interaction
This paper presents the first step in designing a speech-enabled robot that is capable of natural management of miscommunication. It describes the methods
and results of two WOz studies, in which
dyads of naĆÆve participants interacted in a
collaborative task. The first WOz study
explored human miscommunication
management. The second study investigated
how shared visual space and monitoring
shape the processes of feedback and communication in task-oriented interactions.
The results provide insights for the development of human-inspired and
robust natural language interfaces in robots
Introduction for speech and language for interactive robots
This special issue includes research articles which apply spoken language processing to robots that interact with human users through speech, possibly combined with other modalities. Robots that can listen to human speech, understand it, interact according to the conveyed meaning, and respond represent major research and technological challenges. Their common aim is to equip robots with natural interaction abilities. However, robotics and spoken language processing are areas that are typically studied within their respective communities with limited communication across disciplinary boundaries. The articles in this special issue represent examples that address the need for an increased multidisciplinary exchange of ideas
Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models
We investigate the task of building open domain, conversational dialogue
systems based on large dialogue corpora using generative models. Generative
models produce system responses that are autonomously generated word-by-word,
opening up the possibility for realistic, flexible interactions. In support of
this goal, we extend the recently proposed hierarchical recurrent
encoder-decoder neural network to the dialogue domain, and demonstrate that
this model is competitive with state-of-the-art neural language models and
back-off n-gram models. We investigate the limitations of this and similar
approaches, and show how its performance can be improved by bootstrapping the
learning from a larger question-answer pair corpus and from pretrained word
embeddings.Comment: 8 pages with references; Published in AAAI 2016 (Special Track on
Cognitive Systems
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