A novel approach to proactive human-robot cooperation

Schrempf, Oliver C.; Hanebeck, Uwe D.; Schmid, Andreas; Wörn, Heinz

doi:10.1109/roman.2005.1513838

Cited by 48 publications

(28 citation statements)

References 5 publications

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“…Proactive agents that infer intentions from e.g. non-verbal, or contextual cues can potentially offer more intuitive collaboration with humans [19] [20]. System autonomy however has a tensive relationship with predictability and user control [21] [22].…”

Section: Autonomymentioning

confidence: 99%

‘Give me a hug’: the effects of touch and autonomy on people's responses to embodied social agents

Cramer

Kemper²,

Amin³

et al. 2009

Computer Animation & Virtual

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Embodied social agents are programmed to display human‐like social behaviour to increase intuitiveness of interacting with these agents. It is yet unclear what the differences in peoples' responses are to different types of agents' social behaviours. One example is touch. Despite robots' physical embodiment and increasing autonomy, the effect of communicative touch has been a mostly overlooked aspect of human‐robot interaction. This video‐based, 2 × 2 between‐subject survey experiment (N = 119) found that the combination of touch and proactivity influenced whether people saw the robot as machine‐like and dependable. Furthermore, participants' attitude toward robots in general was found to influence perceived closeness between a human and a robot. Results show that communicative touch could be considered a more appropriate behaviour for proactive agents rather than reactive agents. Also, people that are generally more positive towards robots find robots that interact by touch less machine‐like. These effects illustrate that careful consideration is necessary when incorporating social behaviours in agents' physical interaction design. Copyright © 2009 John Wiley & Sons, Ltd.

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Section: Autonomymentioning

confidence: 99%

‘Give me a hug’: the effects of touch and autonomy on people's responses to embodied social agents

Cramer

Kemper²,

Amin³

et al. 2009

Computer Animation & Virtual

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“…Some authors extends the HMI paradigm developing frameworks for interaction between human and domestics robots [14,24]. Lee et al [14] define three main modules in their architecture.…”

Section: Related Workmentioning

confidence: 99%

“…One module is destined to multi-modal interaction, which provides many interaction ways between the users; the other one is a task oriented module, known as a cognitive module; and finally a module that deals with the emotions in the interaction with human. Schrempf [24] allows the planning of the robots actions even if information about the human intentions are uncertain, introducing the concept of pro-active execution of tasks. The idea of bridging the real world to the virtual one is not new.…”

Section: Related Workmentioning

confidence: 99%

The H-N2N framework: towards providing interperception in massive applications

Burlamaqui

Azevedo

Dantas

et al. 2009

Multimed Tools Appl

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We propose a framework with a flexible architecture that have been designed and implemented for collaborative interaction of users, to be applied in massive applications through the Web. We introduce the concept of interperception and use technologies as massive virtual environments and teleoperation for the creation of environments (mixing virtual and real ones) in order to promote accessibility and transparency in the interaction between people, and between people and animate devices (such as robots) through the Web. Experiments with massive games, with interactive applications in digital television, with users and robots interacting in virtual and real versions of museums and cultural centers are presented to validate our proposal.

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“…Intention recognition [1] is the process of estimating the force driving human actions based on noisy observations of the human's interactions with his environment. For example, a robot embedded in a household can assist the human at its best, when estimating whether the human wants to cook, wash, etc., based on observations such as his location, grasping activity, and object interactions [2]. In general, approaches to intention recognition may be categorized into symbolic approaches [3], probabilistic approaches, [4], [5] and blends thereof [6].…”

Section: Introductionmentioning

confidence: 99%

“…The key difference between symbolic and probabilistic approaches is that in the former the possibility of an intention is deduced, while in the latter the probability of an intention is inferred. In this paper, intention recognition is considered a discrete-time state estimation problem formalized in a Dynamic Bayesian Networks (DBN) [7], with a state containing the set of intentions to be estimated [2], [8], [9]. In order to allow for an intuitive interactive cooperation, efficient online inference in these models must be achieved.…”

Section: Introductionmentioning

confidence: 99%

Situation-Specific Intention Recognition for Human-Robot Cooperation

Krauthausen

Hanebeck

2010

KI 2010: Advances in Artificial Intelligence

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Abstract. Recognizing human intentions is part of the decision process in many technical devices. In order to achieve natural interaction, the required estimation quality and the used computation time need to be balanced. This becomes challenging, if the number of sensors is high and measurement systems are complex. In this paper, a model predictive approach to this problem based on online switching of small, situation-specific Dynamic Bayesian Networks is proposed. The contributions are an efficient modeling and inference of situations and a greedy model predictive switching algorithm maximizing the mutual information of predicted situations. The achievable accuracy and computational savings are demonstrated for a household scenario by using an extended range telepresence system.

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A novel approach to proactive human-robot cooperation

Cited by 48 publications

References 5 publications

‘Give me a hug’: the effects of touch and autonomy on people's responses to embodied social agents

‘Give me a hug’: the effects of touch and autonomy on people's responses to embodied social agents

The H-N2N framework: towards providing interperception in massive applications

Situation-Specific Intention Recognition for Human-Robot Cooperation

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