An Integrated Model of Speech to Arm Gestures Mapping in Human-Robot Interaction

International audienceIn multimodal human-robot interaction (HRI), the process of communication can be established through verbal, non-verbal, and/or para-verbal cues. The linguistic literature shows that para-verbal and non-verbal communications are naturally synchronized, however the natural mechnisam of this synchronization is still largely unexplored. This research focuses on the relation between non-verbal and para-verbal communication by mapping prosody cues to the corresponding metaphoric arm gestures. Our approach for synthesizing arm gestures uses the coupled hidden Markov models (CHMM), which could be seen as a collection of HMM characterizing the segmented prosodic characteristics' stream and the segmented rotation characteristics' streams of the two arms articulations. Experimental results with Nao robot are reported

Research on Application of Cognitive-Driven Human-Computer Interaction

Human-computer interaction is an important research content of intelligent manufacturing human factor engineering. Natural human-computer interaction conforms to the cognition of users' habits and can efficiently process inaccurate information interaction, thus improving user experience and reducing cognitive load. Through the analysis of the information interaction process, user interaction experience cognition and human-computer interaction principles in the human-computer interaction system, a cognitive-driven human-computer interaction information transmission model is established. Investigate the main interaction modes in the current human-computer interaction system, and discuss its application status, technical requirements and problems. This paper discusses the analysis and evaluation methods of interaction modes in human-computer system from three levels of subjective evaluation, physiological measurement and mathematical method evaluation, so as to promote the understanding of inaccurate information to achieve the effect of interaction self-adaptation and guide the design and optimization of human-computer interaction system. According to the development status of human-computer interaction in intelligent environment, the research hotspots, problems and development trends of human-computer interaction are put forward

Who am I talking with? A face memory for social robots

In order to provide personalized services and to develop human-like interaction capabilities robots need to rec- ognize their human partner. Face recognition has been studied in the past decade exhaustively in the context of security systems and with significant progress on huge datasets. However, these capabilities are not in focus when it comes to social interaction situations. Humans are able to remember people seen for a short moment in time and apply this knowledge directly in their engagement in conversation. In order to equip a robot with capabilities to recall human interlocutors and to provide user- aware services, we adopt human-human interaction schemes to propose a face memory on the basis of active appearance models integrated with the active memory architecture. This paper presents the concept of the interactive face memory, the applied recognition algorithms, and their embedding into the robot’s system architecture. Performance measures are discussed for general face databases as well as scenario-specific datasets

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

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

a human in the loop cyber physical system for collaborative assembly in smart manufacturing

Abstract Industry 4.0 rose with the introduction of cyber-physical systems (CPS) and Internet of things (IoT) inside manufacturing systems. CPS represent self-controlled physical processes, having tight networking capabilities and efficient interfaces for human interaction. The interactive dimension of CPS reaches its maximum when defined in terms of natural human-machine interfaces (NHMI), i.e., those reducing the technological barriers required for the interaction. This paper presents a NHMI bringing the human decision-making capabilities inside the cybernetic control loop of a smart manufacturing assembly system. The interface allows to control, coordinate and cooperate with an industrial cobot during the task execution