Interactive Multimodal Robot Programming

Iba, Soshi; Paredis, Christiaan J. J.; Khosla, Pradeep K.

doi:10.1177/0278364904049250

Cited by 44 publications

(9 citation statements)

References 46 publications

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“…Natural human interfaces for richer HRI have been explored for service robots. Vision-based interfaces in mobile robot navigation [18,[27][28][29], haptic feedback interfaces in tele-operations and surgeries [3,30,31], voice-based interfaces [32,33], and multimodal interfaces which integrate two or more interaction modals [34,35], are some of the most commonly used approaches.…”

Section: Hri Modals and Devicesmentioning

confidence: 99%

Novel AR-based interface for human-robot interaction and visualization

2014

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Intuitive and efficient interfaces for humanrobot interaction (HRI) has been a challenging issue in robotics as it is essential for the prevalence of robots supporting humans in key areas of activities. This paper presents a novel augmented reality (AR) based interface to facilitate human-virtual robot interaction. A number of human-virtual robot interaction methods have been formulated and implemented with respect to the various types of operations needed in different robotic applications. A Euclidean distance-based method is developed to assist the users in the interaction with the virtual robot and the spatial entities in an AR environment. A monitor-based visualization mode is adopted as it enables the users to perceive the virtual contents associated with different interaction methods, and the virtual content augmented in the real environment is informative and useful to the users during their interaction with the virtual robot. Case researches are presented to demonstrate the successful implementation of the AR-based HRI interface in planning robot pick-and-place operations and path following operations.

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Section: Hri Modals and Devicesmentioning

confidence: 99%

Novel AR-based interface for human-robot interaction and visualization

2014

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“…The use of gestures to guide robots (both humanoids and non-humanoids) attracted much attention in the recent ten years [Triesch and von der Malsburg, 1998;Nickel and Stiefelhagen, 2007;Iba et al, 2005]. Xu et al [2007] compared the effectiveness of gesture interface to using a joystick for controlling a miniature robot.…”

Section: Related Workmentioning

confidence: 99%

“…The first question that has to be answered is what kind of nonverbal behavior and gestures will be used by the human operator? Answering this question is essential as most gesture recognition systems require a pre-specified set of gestures to be recognized [Xu et al, 2007;Iba et al, 2005]. The first goal of this study is to shed light on the patterns of gesture usage during a well-specified interaction scenario, which is collaborative navigation.…”

Section: Introductionmentioning

confidence: 99%

Interaction Between Untrained Users and a Miniature Robot in a Collaborative Navigation Controlled Experiment

Mohammad

Nishida

2008

Int. J. Inf. Acquisition

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Future robots are expected to be operated by untrained persons using natural means of communication. This entails the necessity of understanding how humans will communicate with such robots especially in the non-humanoid case where anthropomorphism is in its minimum. This paper presents a controlled experiment to study the interaction between untrained human users and a miniature robot in a collaborative navigation task. Three dimensions of the interaction are studied. The first dimensions is the gesture use by the operators during the interaction. A consistent usage pattern with slight cultural effect was discovered in the data. The second dimension is human adaptation to both the task requirements and robot capabilities. Three main findings about the human adaptation are reported. The third studied dimension is the effectiveness and naturalness of using motion cues as a feedback mechanism from the robot in comparison with verbal feedback. The results of the experiment showed that there is no significant difference in the task completion accuracy and time or in the feeling of naturalness between motion cues and verbal feedback, and there is a statistically significant improvement when using either of them compared with the control case. Moreover, the subjects selected the motion cues feedback mechanism more frequently as the preferred feedback modality for them. These findings support the use of nonverbal motion feedback for knowledge transfer and information acquisition from non-humanoid robots in collaborative navigation tasks.

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“…The new skill built by the user is represented as a Sequence Function Chart (SFC) that makes it possible to build multiple programming structures, such as single sequences, sequence selection (selection mode), simultaneous sequences (parallel mode) and loops, and not just a serial sequence of actions, as in another similar works like [11], [12] or [13].…”

Section: Introductionmentioning

confidence: 99%

“…In [12] a vacuum robot is programmed for a room cleaning task. Verbal commands and gestures are translated into a sequence of cleaning actions.…”

Section: Introductionmentioning

confidence: 99%

End-user programming of a social robot by dialog

Gorostiza

Salichs

2011

Robotics and Autonomous Systems

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One of the main challenges faced by social robots is how to provide intuitive, natural and enjoyable usability for the end-user. In our ordinary environment, social robots could be important tools for education and entertainment (edutainment) in a variety of ways. This paper presents a Natural Programming System (NPS) that is geared to non-expert users. The main goal of such a system is to provide an enjoyable interactive platform for the users to build different programs within their social robot platform. The end-user can build a complex net of actions and conditions (a sequence) in a social robot via mixed-initiative dialogs and multimodal interaction. The system has been implemented and tested in Maggie, a real social robot with multiple skills, conceived as a general HRI researching platform. The robot's internal features (skills) have been implemented to be verbally accessible to the end-user, who can combine them into others more complex following a bottom-up model. The built sequence is internally implemented as a Sequence Function Chart (SFC), which allows parallel execution, modularity and re-use. A multimodal Dialog Manager System (DMS) takes charge of keeping the coherence of the interaction. This work is thought for bringing social robots closer to non-expert users, who can play the game of "teaching how to do things" with the robot.

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Interactive Multimodal Robot Programming

Abstract: Abstract

Cited by 44 publications

References 46 publications

Novel AR-based interface for human-robot interaction and visualization

Novel AR-based interface for human-robot interaction and visualization

Interaction Between Untrained Users and a Miniature Robot in a Collaborative Navigation Controlled Experiment

End-user programming of a social robot by dialog

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