Exploiting the robot kinematic redundancy for emotion conveyance to humans as a lower priority task

Current approaches do not allow robots to execute a task and simultaneously convey emotions to users using their body motions. This paper explores the capabilities of the Jacobian null space of a humanoid robot to convey emotions. A task priority formulation has been implemented in a Pepper robot which allows the specification of a primary task (waving gesture, transportation of an object, etc.) and exploits the kinematic redundancy of the robot to convey emotions to humans as a lower priority task. The emotions, defined by Mehrabian as points in the pleasure–arousal–dominance space, generate intermediate motion features (jerkiness, activity and gaze) that carry the emotional information. A map from this features to the joints of the robot is presented. A user study has been conducted in which emotional motions have been shown to 30 participants. The results show that happiness and sadness are very well conveyed to the user, calm is moderately well conveyed, and fear is not well conveyed. An analysis on the dependencies between the motion features and the emotions perceived by the participants shows that activity correlates positively with arousal, jerkiness is not perceived by the user, and gaze conveys dominance when activity is low. The results indicate a strong influence of the most energetic motions of the emotional task and point out new directions for further research. Overall, the results show that the null space approach can be regarded as a promising mean to convey emotions as a lower priority task.Postprint (author's final draft

A Review of Verbal and Non-Verbal Human-Robot Interactive Communication

In this paper, an overview of human-robot interactive communication is presented, covering verbal as well as non-verbal aspects of human-robot interaction. Following a historical introduction, and motivation towards fluid human-robot communication, ten desiderata are proposed, which provide an organizational axis both of recent as well as of future research on human-robot communication. Then, the ten desiderata are examined in detail, culminating to a unifying discussion, and a forward-looking conclusion

Expressivity in Natural and Artificial Systems

Roboticists are trying to replicate animal behavior in artificial systems. Yet, quantitative bounds on capacity of a moving platform (natural or artificial) to express information in the environment are not known. This paper presents a measure for the capacity of motion complexity -- the expressivity -- of articulated platforms (both natural and artificial) and shows that this measure is stagnant and unexpectedly limited in extant robotic systems. This analysis indicates trends in increasing capacity in both internal and external complexity for natural systems while artificial, robotic systems have increased significantly in the capacity of computational (internal) states but remained more or less constant in mechanical (external) state capacity. This work presents a way to analyze trends in animal behavior and shows that robots are not capable of the same multi-faceted behavior in rich, dynamic environments as natural systems.Comment: Rejected from Nature, after review and appeal, July 4, 2018 (submitted May 11, 2018

Multimodal Adapted Robot Behavior Synthesis within a Narrative Human-Robot Interaction

International audienceIn human-human interaction, three modalities of communication (i.e., verbal, nonverbal, and paraverbal) are naturally coordinated so as to enhance the meaning of the conveyed message. In this paper, we try to create a similar coordination between these modalities of communication in order to make the robot behave as naturally as possible. The proposed system uses a group of videos in order to elicit specific target emotions in a human user, upon which interactive narratives will start (i.e., interactive discussions between the participant and the robot around each video's content). During each interaction experiment, the humanoid expressive ALICE robot engages and generates an adapted multimodal behavior to the emotional content of the projected video using speech, head-arm metaphoric gestures, and/or facial expressions. The interactive speech of the robot is synthesized using Mary-TTS (text to speech toolkit), which is used-in parallel-to generate adapted head-arm gestures [1]. This synthesized multimodal robot behavior is evaluated by the interacting human at the end of each emotion-eliciting experiment. The obtained results validate the positive effect of the generated robot behavior multimodality on interaction

Analyzing Input and Output Representations for Speech-Driven Gesture Generation

This paper presents a novel framework for automatic speech-driven gesture generation, applicable to human-agent interaction including both virtual agents and robots. Specifically, we extend recent deep-learning-based, data-driven methods for speech-driven gesture generation by incorporating representation learning. Our model takes speech as input and produces gestures as output, in the form of a sequence of 3D coordinates. Our approach consists of two steps. First, we learn a lower-dimensional representation of human motion using a denoising autoencoder neural network, consisting of a motion encoder MotionE and a motion decoder MotionD. The learned representation preserves the most important aspects of the human pose variation while removing less relevant variation. Second, we train a novel encoder network SpeechE to map from speech to a corresponding motion representation with reduced dimensionality. At test time, the speech encoder and the motion decoder networks are combined: SpeechE predicts motion representations based on a given speech signal and MotionD then decodes these representations to produce motion sequences. We evaluate different representation sizes in order to find the most effective dimensionality for the representation. We also evaluate the effects of using different speech features as input to the model. We find that mel-frequency cepstral coefficients (MFCCs), alone or combined with prosodic features, perform the best. The results of a subsequent user study confirm the benefits of the representation learning.Comment: Accepted at IVA '19. Shorter version published at AAMAS '19. The code is available at https://github.com/GestureGeneration/Speech_driven_gesture_generation_with_autoencode

Gestures in human-robot interaction

Gesten sind ein Kommunikationsweg, der einem Betrachter Informationen oder Absichten übermittelt. Daher können sie effektiv in der Mensch-Roboter-Interaktion, oder in der Mensch-Maschine-Interaktion allgemein, verwendet werden. Sie stellen eine Möglichkeit für einen Roboter oder eine Maschine dar, um eine Bedeutung abzuleiten. Um Gesten intuitiv benutzen zukönnen und Gesten, die von Robotern ausgeführt werden, zu verstehen, ist es notwendig, Zuordnungen zwischen Gesten und den damit verbundenen Bedeutungen zu definieren -- ein Gestenvokabular. Ein Menschgestenvokabular definiert welche Gesten ein Personenkreis intuitiv verwendet, um Informationen zu übermitteln. Ein Robotergestenvokabular zeigt welche Robotergesten zu welcher Bedeutung passen. Ihre effektive und intuitive Benutzung hängt von Gestenerkennung ab, das heißt von der Klassifizierung der Körperbewegung in diskrete Gestenklassen durch die Verwendung von Mustererkennung und maschinellem Lernen. Die vorliegende Dissertation befasst sich mit beiden Forschungsbereichen. Als eine Voraussetzung für die intuitive Mensch-Roboter-Interaktion wird zunächst ein Aufmerksamkeitsmodell für humanoide Roboter entwickelt. Danach wird ein Verfahren für die Festlegung von Gestenvokabulare vorgelegt, das auf Beobachtungen von Benutzern und Umfragen beruht. Anschliessend werden experimentelle Ergebnisse vorgestellt. Eine Methode zur Verfeinerung der Robotergesten wird entwickelt, die auf interaktiven genetischen Algorithmen basiert. Ein robuster und performanter Gestenerkennungsalgorithmus wird entwickelt, der auf Dynamic Time Warping basiert, und sich durch die Verwendung von One-Shot-Learning auszeichnet, das heißt durch die Verwendung einer geringen Anzahl von Trainingsgesten. Der Algorithmus kann in realen Szenarien verwendet werden, womit er den Einfluss von Umweltbedingungen und Gesteneigenschaften, senkt. Schließlich wird eine Methode für das Lernen der Beziehungen zwischen Selbstbewegung und Zeigegesten vorgestellt.Gestures consist of movements of body parts and are a mean of communication that conveys information or intentions to an observer. Therefore, they can be effectively used in human-robot interaction, or in general in human-machine interaction, as a way for a robot or a machine to infer a meaning. In order for people to intuitively use gestures and understand robot gestures, it is necessary to define mappings between gestures and their associated meanings -- a gesture vocabulary. Human gesture vocabulary defines which gestures a group of people would intuitively use to convey information, while robot gesture vocabulary displays which robot gestures are deemed as fitting for a particular meaning. Effective use of vocabularies depends on techniques for gesture recognition, which considers classification of body motion into discrete gesture classes, relying on pattern recognition and machine learning. This thesis addresses both research areas, presenting development of gesture vocabularies as well as gesture recognition techniques, focusing on hand and arm gestures. Attentional models for humanoid robots were developed as a prerequisite for human-robot interaction and a precursor to gesture recognition. A method for defining gesture vocabularies for humans and robots, based on user observations and surveys, is explained and experimental results are presented. As a result of the robot gesture vocabulary experiment, an evolutionary-based approach for refinement of robot gestures is introduced, based on interactive genetic algorithms. A robust and well-performing gesture recognition algorithm based on dynamic time warping has been developed. Most importantly, it employs one-shot learning, meaning that it can be trained using a low number of training samples and employed in real-life scenarios, lowering the effect of environmental constraints and gesture features. Finally, an approach for learning a relation between self-motion and pointing gestures is presented

Tactile Interactions with a Humanoid Robot : Novel Play Scenario Implementations with Children with Autism

Acknowledgments: This work has been partially supported by the European Commission under contract number FP7-231500-ROBOSKIN. Open Access: This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.The work presented in this paper was part of our investigation in the ROBOSKIN project. The project has developed new robot capabilities based on the tactile feedback provided by novel robotic skin, with the aim to provide cognitive mechanisms to improve human-robot interaction capabilities. This article presents two novel tactile play scenarios developed for robot-assisted play for children with autism. The play scenarios were developed against specific educational and therapeutic objectives that were discussed with teachers and therapists. These objectives were classified with reference to the ICF-CY, the International Classification of Functioning – version for Children and Youth. The article presents a detailed description of the play scenarios, and case study examples of their implementation in HRI studies with children with autism and the humanoid robot KASPAR.Peer reviewedFinal Published versio