Armar Ii — A Learning and Cooperative Multimodal Humanoid Robot System

Dillmann, Rüdiger; Becher, Regine; Steinhaus, Peter

doi:10.1142/s0219843604000046

Cited by 32 publications

(13 citation statements)

References 5 publications

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“…[35,6]) explore aspects of gestural behavior in human-ECA interactions. Physically embodied ECAs-for example, ARMAR II [9,10] and Leo [5]-incorporate the ability to perform articulated body tracking and recognize human gestures.…”

Section: Previous Workmentioning

confidence: 99%

Recognizing gaze aversion gestures in embodied conversational discourse

Morency

Christoudias

Darrell

2006

Proceedings of the 8th International Conference on Multimodal Interfaces

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Eye gaze offers several key cues regarding conversational discourse during face-to-face interaction between people. While a large body of research results exist to document the use of gaze in humanto-human interaction, and in animating realistic embodied avatars, recognition of conversational eye gestures-distinct eye movement patterns relevant to discourse-has received less attention. We analyze eye gestures during interaction with an animated embodied agent and propose a non-intrusive vision-based approach to estimate eye gaze and recognize eye gestures. In our user study, human participants avert their gaze (i.e. with "look-away" or "thinking" gestures) during periods of cognitive load. Using our approach, an agent can visually differentiate whether a user is thinking about a response or is waiting for the agent or robot to take its turn.

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Section: Previous Workmentioning

confidence: 99%

Recognizing gaze aversion gestures in embodied conversational discourse

Morency

Christoudias

Darrell

2006

Proceedings of the 8th International Conference on Multimodal Interfaces

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“…[19,3]) are exploring aspects of gestural behavior in human-ECA interactions. Physically embodied ECAs-for example, ARMAR II [5,6] and Leo [2]-have also begun to incorporate the ability to perform articulated body tracking and recognize human gestures.…”

Section: Related Workmentioning

confidence: 99%

Contextual recognition of head gestures

Morency

Sidner

Lee

et al. 2005

Proceedings of the 7th International Conference on Multimodal Interfaces

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Head pose and gesture offer several key conversational grounding cues and are used extensively in face-to-face interaction among people. We investigate how dialog context from an embodied conversational agent (ECA) can improve visual recognition of user gestures. We present a recogntion framework which (1) extracts contextual features from an ECAś dialog manager, (2) computes a predicition of head nod and head shakes, and (3) integrates the contextual predictions with the visual observation of a vision-based head gesture recognizer. We found a subset of lexical, punctuation and timing features that are easily available in most ECA architectures and can be used to learn how to predict user feedback. Using a discriminative approach to contextual prediction and multi-modal integration, we were able to improve the performancae of head gesture detection even when the topic of the test set was significantly different than the training set. International Conference on Multimodal Interfaces (ICMI)This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. ABSTRACTHead pose and gesture offer several key conversational grounding cues and are used extensively in face-to-face interaction among people. We investigate how dialog context from an embodied conversational agent (ECA) can improve visual recognition of user gestures. We present a recognition framework which (1) extracts contextual features from an ECA's dialog manager, (2) computes a prediction of head nod and head shakes, and (3) integrates the contextual predictions with the visual observation of a vision-based head gesture recognizer. We found a subset of lexical, punctuation and timing features that are easily available in most ECA architectures and can be used to learn how to predict user feedback. Using a discriminative approach to contextual prediction and multi-modal integration, we were able to improve the performance of head gesture detection even when the topic of the test set was significantly different than the training set.

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“…Fig.9 shows motion generation and behavior verification in washing dish experiment. (1), (3), (5), (6) shows motion generation phase using planner. (2) shows the robot examines if there exists water flows by navigating visual attention around the water outlet.…”

Section: B Visual Navigation and Processing For Behavior Verificationmentioning

confidence: 99%

“…There exists tool-handling research using hand eye systems in early robotics researches on assembly tasks in block world, however there is few for current research using humanoid robot in daily-life environment tasks with tools and objects that we use in everyday life [6]- [8]. We have developed dailylife environment behavior of humanoid robot as sweeping, vacuuming, pouring water from kettle, carrying tray, using washing machine, dish washing and so on [9].…”

Section: Introductionmentioning

confidence: 99%

Vision based behavior verification system of humanoid robot for daily environment tasks

Okada

Kojima

Sagawa

et al. 2006

2006 6th IEEE-RAS International Conference on Humanoid Robots

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Abstract-This paper describes integrated/intelligent humanoid robot system for daily-life environment tasks. We have realized complex behaviors of a humanoid robot in dailylife environment based on motion planner technique using an environment and manipulation knowledge. However in order to adapt to unknown or dynamic situations, sensor based behavior variation is essentially important. In this paper, we present a design and implementation of sensor based behavior verification system using an environment and manipulation knowledge, which is also used in manipulation motion planner. We also present software architecture that allows us to write a single stream code to perform complex concurrent humanoid motions. By using this architecture, sensor based verification functions are easily integrated in motion generation functions. Finally, we demonstrated a water-pouring task and a dishwashing task of the life-sized humanoid robot HRP2-JSK in a real environment while verifying its own motion.

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Armar Ii — A Learning and Cooperative Multimodal Humanoid Robot System

Cited by 32 publications

References 5 publications

Recognizing gaze aversion gestures in embodied conversational discourse

Recognizing gaze aversion gestures in embodied conversational discourse

Contextual recognition of head gestures

Vision based behavior verification system of humanoid robot for daily environment tasks

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