Nakadai et al. (2001) have developed a real-time auditory and visual multiple-talker tracking technique. In this paper, this technique is applied to human-robot interaction including a receptionist robot and a compani...
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Nakadai et al. (2001) have developed a real-time auditory and visual multiple-talker tracking technique. In this paper, this technique is applied to human-robot interaction including a receptionist robot and a companion robot at a party. The system includes face identification, speech recognition, focus-of-attention control, and sensorimotor task in tracking multiple talkers. The system is implemented on a upper-torso humanoid and the talker tracking is attained by distributed processing on three nodes connected by 100Base-TX network. The delay of tracking is 200 msec. Focus-of-attention is controlled by associating auditory and visual streams by using the sound source direction and talker position as a clue. Once an association is established, the humanoid keeps its face to the direction of the associated talker.
This paper presents a case study of edutainment robot, which is an intelligent robot for educational use with a voice-QA model applied. The emphatic functions of our robot are: analyzing spoken question from a student...
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This paper presents a case study of edutainment robot, which is an intelligent robot for educational use with a voice-QA model applied. The emphatic functions of our robot are: analyzing spoken question from a student, finding an appropriate answer in Korean encyclopedia, and then serving the answer with speech synthesis. We develop the ESTk, which is an Automatic Speech Recognition (ASR) system based on Finite State Network (FSN) for processing Korean spoken questions. For answer extraction, we utilize machine learning techniques and pattern extraction method. With our live-update interaction method, our robot can be extended with new knowledge in real-time. By conducting a quiz game, we show a possibility of our robot as an edutainment robot.
For a robot to be able to first understand and then achieve a human's goals, it must be able to reason about a) the context of the current situation (with respect to which it must interpret the human's command...
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For a robot to be able to first understand and then achieve a human's goals, it must be able to reason about a) the context of the current situation (with respect to which it must interpret the human's commands) and b) the future world state (as intended by the human) and how to achieve it. Since humans express their intentions and plans using qualitative symbolic representations, robots must be enabled to reason and interact on the same representational level. In this paper, we describe the use of classical AI planning techniques for situation-aware interpretation and execution of human commands. We show how, based on a planning domain, a robot can be enabled to understand commands in natural language, plan for their situation-dependent realization and revise its plans based on new perceptions. We show the effectiveness of this approach in several HRI scenarios modeled as planning domains as well as with examples from a real robot system developed in the EU-funded CoSy project.
In this paper we propose a trainable system that learns grounded language models from examples with a minimum of user intervention and without feedback. We have focused on the acquisition of grounded meanings of spati...
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In this paper we propose a trainable system that learns grounded language models from examples with a minimum of user intervention and without feedback. We have focused on the acquisition of grounded meanings of spatial and adjective/noun terms. The system has been used to understand and subsequently to generate appropriate natural language descriptions of real objects and to engage in verbal interactions with a human partner. We have also addressed the problem of resolving eventual ambiguities arising during verbal interaction through an information theoretic approach.
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