Designing a Personality-Driven Robot for a Human-Robot Interaction Scenario

Mohammadi, Hadi Beik; Xirakia, Nikoletta; Abawi, Fares; Barykina, I. N.; Chandran, Krishnan; Nair, Gitanjali; Nguyen, Cuong; Speck, Daniel; Alpay, Tayfun; Griffiths, Sascha; Heinrich, S.; Strahl, Erik; Weber, Cornelius; Wermter, Stefan

doi:10.1109/icra.2019.8793770

Cited by 10 publications

(7 citation statements)

References 30 publications

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“…In [71], the effects of nonverbal communication on trust, a main driver for explainable AI research in HRI, are examined. The work summarized in [6] combine verbal utterances and non-verbal cues to Fig. 3 NICO, the Neuro-Inspired COmpanion communicate emotions, demonstrating the utility of multimodal communication.…”

Section: Re-analysis Of Hri Experiments With Nico From An Xai Perspec...mentioning

confidence: 96%

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What’s on Your Mind, NICO?

et al. 2022

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Explainable AI has become an important field of research on neural machine learning models. However, most existing methods are designed as tools that provide expert users with additional insights into their models. In contrast, in human-robot interaction scenarios, non-expert users are frequently confronted with complex, embodied AI systems whose inner workings are unknown. Therefore, eXplainable Human-Robot Interaction (XHRI) should leverage the user’s intuitive ability to collaborate and to use efficient communication. Using NICO, the Neuro-Inspired COmpanion, as a use-case study, we propose an XHRI framework and show how different types of explanations enhance the interaction experience. These explanations range from (a) non-verbal cues for simple and intuitive feedback of inner states via (b) comprehensive verbal explanations of the robot’s intentions, knowledge and reasoning to (c) multimodal explanations using visualizations, speech and text. We revisit past HRI-related studies conducted with NICO and analyze them with the proposed framework. Furthermore, we present two novel XHRI approaches to extract suitable verbal and multimodal explanations from neural network modules in an HRI scenario.

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Section: Re-analysis Of Hri Experiments With Nico From An Xai Perspec...mentioning

confidence: 96%

“…Emotionally Engaging HRI: To assess the change in human perception of a robot expressing emotions, the difference between a socially engaging and a competitive robot was researched by Beik-Mohammadi et al [6]. In the experiment, a human and NICO played a competitive game.…”

Section: Non-verbal Communication: the Role Of Displaying And Recogni...mentioning

confidence: 99%

What’s on Your Mind, NICO?

et al. 2022

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“…The authors found that a personalized system is rated as more intelligent and likable but receives less social acceptance. Beik-Mohammadi et al (2019) evaluated the influence of two different robot personalities (socially engaged vs. competitive) on user acceptance in the context of a dice game. To show social engagement, NICO makes jokes and engages itself in small talk via a natural language dialogue system; it also uses physical gestures.…”

Section: Human-robot Interaction Evaluationmentioning

confidence: 99%

Teaching NICO How to Grasp: An Empirical Study on Crossmodal Social Interaction as a Key Factor for Robots Learning From Humans

Kerzel¹,

Pekarek-Rosin²,

Strahl³

et al. 2020

Front. Neurorobot.

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To overcome novel challenges in complex domestic environments, humanoid robots can learn from human teachers. We propose that the capability for social interaction should be a key factor in this teaching process and benefits both the subjective experience of the human user and the learning process itself. To support our hypothesis, we present a Human-Robot Interaction study on human-assisted visuomotor learning with the robot NICO, the Neuro-Inspired COmpanion, a child-sized humanoid. NICO is a flexible, social platform with sensing and manipulation abilities. We give a detailed description of NICO's design and a comprehensive overview of studies that use or evaluate NICO. To engage in social interaction, NICO can express stylized facial expressions and utter speech via an Embodied Dialogue System. NICO is characterized in particular by combining these social interaction capabilities with the abilities for human-like object manipulation and crossmodal perception. In the presented study, NICO acquires visuomotor grasping skills by interacting with its environment. In contrast to methods like motor babbling, the learning process is, in part, supported by a human teacher. To begin the learning process, an object is placed into NICO's hand, and if this object is accidentally dropped, the human assistant has to recover it. The study is conducted with 24 participants with little or no prior experience with robots. In the robot-guided experimental condition, assistance is actively requested by NICO via the Embodied Dialogue System. In the human-guided condition, instructions are given by a human experimenter, while NICO remains silent. Evaluation using established questionnaires like Godspeed, Mind Perception, and Uncanny Valley Indices, along with a structured interview and video analysis of the interaction, show that the robot's active requests for assistance foster the participant's engagement and benefit the learning process. This result supports the hypothesis that the ability for social interaction is a key factor for companion robots that learn with the help of non-expert teachers, as these robots become capable of communicating active requests or questions that are vital to their learning process. We also show how the design of NICO both enables and is driven by this approach.

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“…This delay introduces inconsistency or mismatch between input and avatar system that falsifies the provided feedback. For example, the teleoperator may receive the haptic feedback well after the avatar robot has already collided with an object in the remote environment, causing damage to the target robot 1 Institute of Robotics and Mechatronics, German Aerospace Center, Munich, Germany (Hadi.Beikmohammadi, Thomas.Hulin, Annika.Schmidt, Benedikt.Pleintinger, Philipp.Reisich, Aaron.Pereira, Neal.Lii)@DLR.de Fig. 1.…”

Section: Introductionmentioning

confidence: 99%

“…The robot configurations can be pre-recorded and used as a look-up table. The corresponding configurations can be selected based on the closest object position to the current position [1]. To reduce the inconsistency between configurations, end-to-end deep learning with pre-trained neural networks [2] can be used instead of hard-coded look-up tables.…”

Section: Introductionmentioning

confidence: 99%

Model Mediated Teleoperation with a Hand-Arm Exoskeleton in Long Time Delays Using Reinforcement Learning

Beik-Mohammadi

Kerzel

Pleintinger

et al. 2020

2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)

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Telerobotic systems must adapt to new environmental conditions and deal with high uncertainty caused by long-time delays. As one of the best alternatives to human-level intelligence, Reinforcement Learning (RL) may offer a solution to cope with these issues. This paper proposes to integrate RL with the Model Mediated Teleoperation (MMT) concept. The teleoperator interacts with a simulated virtual environment, which provides instant feedback. Whereas feedback from the real environment is delayed, feedback from the model is instantaneous, leading to high transparency. The MMT is realized in combination with an intelligent system with two layers. The first layer utilizes Dynamic Movement Primitives (DMP) which accounts for certain changes in the avatar environment. And, the second layer addresses the problems caused by uncertainty in the model using RL methods. Augmented reality was also provided to fuse the avatar device and virtual environment models for the teleoperator. Implemented on DLR's Exodex Adam hand-arm haptic exoskeleton, the results show RL methods are able to find different solutions when changes are applied to the object position after the demonstration. The results also show DMPs to be effective at adapting to new conditions where there is no uncertainty involved.

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Designing a Personality-Driven Robot for a Human-Robot Interaction Scenario

Cited by 10 publications

References 30 publications

What’s on Your Mind, NICO?

What’s on Your Mind, NICO?

Teaching NICO How to Grasp: An Empirical Study on Crossmodal Social Interaction as a Key Factor for Robots Learning From Humans

Model Mediated Teleoperation with a Hand-Arm Exoskeleton in Long Time Delays Using Reinforcement Learning

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