Grounding the Interaction: Anchoring Situated Discourse in Everyday Human-Robot Interaction

Lemaignan, Séverin; Ros, Raquel; Sisbot, Emrah Akin; Alami, Rachid; Beetz, Michael

doi:10.1007/s12369-011-0123-x

Cited by 73 publications

(42 citation statements)

References 37 publications

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“…Next to social robots that assist people in medical situations [1][2][3], social robots can also help improve the user's spatial cognition through pointing gestures [4][5][6][7], improve the imitation skills of children with autism [8][9][10][11], or greet and communicate with people [12][13][14][15]. Therefore, we argue that one of the most important current challenges in research on social robotics is to develop social robots that can effectively influence human behavior or attitudes.…”

Section: Introductionmentioning

confidence: 99%

Combining Robotic Persuasive Strategies: The Persuasive Power of a Storytelling Robot that Uses Gazing and Gestures

Ham

Cuijpers

Cabibihan

2015

Int J of Soc Robotics

102

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Earlier theorizing suggested that an (artificial) agent that combines persuasive strategies will be more persuasive. Therefore, the current research investigated whether a robot that uses two persuasive strategies is more persuasive than a robot that uses only one. Two crucial persuasive strategies that humans use in face-to-face persuasion are gazing and gestures, and therefore we studied the combined and individual contribution of these two persuasive strategies (gestures and gazing) on the persuasiveness of a storytelling robot. A robot told a classical persuasive story about the consequences of lying to forty-eight participants, and was programmed to use (persuasive) gestures (or not) and gazing (or not). Next, we asked participants to evaluate the character in the story thereby assessing the robot's persuasiveness. Results presented evidence a robot's persuasiveness is increased when gazing is used. When the robot used gestures, its persuasiveness only increased when it also used gazing. When the robot did not use gazing, using gestures diminished the robot's persuasiveness. We discuss the implications for theory and design of robots that are more persuasive.

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Section: Introductionmentioning

confidence: 99%

Combining Robotic Persuasive Strategies: The Persuasive Power of a Storytelling Robot that Uses Gazing and Gestures

Ham

Cuijpers

Cabibihan

2015

Int J of Soc Robotics

102

View full text Add to dashboard Cite

show abstract

“…Grounding symbolic knowledge about actions and objects to a perceived model of the physical world has been addressed in many works over the past decades (for a review of symbol grounding and symbol grounding in relation to perceptual anchoring, in particular, see the report by Coradeschi et al (2013)). Nonetheless, a few notable contributions in grounding of actions are the works presented by Lemaignan et al (2012) and Stramandinoli et al (2011Stramandinoli et al ( , 2012. In the study by Lemaignan et al (2012), the authors presented a grounded shared model of the world that is used for both human-robot verbal and non-verbal interactions.…”

Section: Related Workmentioning

confidence: 99%

“…Nonetheless, a few notable contributions in grounding of actions are the works presented by Lemaignan et al (2012) and Stramandinoli et al (2011Stramandinoli et al ( , 2012. In the study by Lemaignan et al (2012), the authors presented a grounded shared model of the world that is used for both human-robot verbal and non-verbal interactions. The model is realistic in the sense that 3D models of objects are created and maintained in such a way that spatial relations between objects can be used to reasoning upon various concepts, relations, and actions in human-robot interaction scenarios.…”

Section: Related Workmentioning

confidence: 99%

Learning Actions to Improve the Perceptual Anchoring of Objects

2017

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In this paper, we examine how to ground symbols referring to objects in perceptual data from a robot system by examining object entities and their changes over time. In particular, we approach the challenge by (1) tracking and maintaining object entities over time; and (2) utilizing an artificial neural network to learn the coupling between words referring to actions and movement patterns of tracked object entities. For this purpose, we propose a framework that relies on the notations presented in perceptual anchoring. We further present a practical extension of the notation such that our framework can track and maintain the history of detected object entities. Our approach is evaluated using everyday objects typically found in a home environment. Our object classification module has the possibility to detect and classify over several 100 object categories. We demonstrate how the framework creates and maintains, both in space and time, representations of objects such as "spoon" and "coffee mug." These representations are later used for training of different sequential learning algorithms to learn movement actions such as "pour" and "stir." We finally exemplify how learned movements actions, combined with commonsense knowledge, further can be used to improve the anchoring process per se.

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“…It has been especially successful in grounding concepts [9] and objects [42,31] through human-robot interaction. Works like Chu et al [11] look at mapping from text to haptic signals.…”

Section: Related Workmentioning

confidence: 99%

Tell Me Dave: Context-Sensitive Grounding of Natural Language to Manipulation Instructions

Misra

Sung

Lee

et al. 2014

Robotics: Science and Systems X

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Abstract-We consider performing a sequence of mobile manipulation tasks with instructions given in natural language (NL). Given a new environment, even a simple task such as of boiling water would be performed quite differently depending on the presence, location and state of the objects. We start by collecting a dataset of task descriptions in free-form natural language and the corresponding grounded task-logs of the tasks performed in an online robot simulator. We then build a library of verbenvironment-instructions that represents the possible instructions for each verb in that environment-these may or may not be valid for a different environment and task context. We present a model that takes into account the variations in natural language, and ambiguities in grounding them to robotic instructions with appropriate environment context and task constraints. Our model also handles incomplete or noisy NL instructions. Our model is based on an energy function that encodes such properties in a form isomorphic to a conditional random field. In evaluation, we show that our model produces sequences that perform the task successfully in a simulator and also significantly outperforms the state-of-the-art. We also verify by executing our output instruction sequences on a PR2 robot.

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Grounding the Interaction: Anchoring Situated Discourse in Everyday Human-Robot Interaction

Cited by 73 publications

References 37 publications

Combining Robotic Persuasive Strategies: The Persuasive Power of a Storytelling Robot that Uses Gazing and Gestures

Combining Robotic Persuasive Strategies: The Persuasive Power of a Storytelling Robot that Uses Gazing and Gestures

Learning Actions to Improve the Perceptual Anchoring of Objects

Tell Me Dave: Context-Sensitive Grounding of Natural Language to Manipulation Instructions

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