Modeling and evaluating beat gestures for social robots

Zabala, Unai; Rodriguez, Igor; Martínez-Otzeta, José María; Lazkano, Elena

doi:10.1007/s11042-021-11289-x

Cited by 10 publications

(3 citation statements)

References 34 publications

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“…Participants were asked to sit in front of the robot (as shown in Figure 16 ). Immediately after, Qhali began a routine that consisted of gestures and phrases crafted from HRI literature [ 31 , 33 , 45 , 46 ], with collaboration from an experienced entertainer professional. The routine started with Qhali’s introduction and transitioned to the introduction of the therapist.…”

Section: Testing the Qhali Robot In A Controlled Environmentmentioning

confidence: 99%

Qhali: A Humanoid Robot for Assisting in Mental Health Treatment

Pérez-Zuñiga,

Arce,

Gibaja

et al. 2024

Sensors

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In recent years, social assistive robots have gained significant acceptance in healthcare settings, particularly for tasks such as patient care and monitoring. This paper offers a comprehensive overview of the expressive humanoid robot, Qhali, with a focus on its industrial design, essential components, and validation in a controlled environment. The industrial design phase encompasses research, ideation, design, manufacturing, and implementation. Subsequently, the mechatronic system is detailed, covering sensing, actuation, control, energy, and software interface. Qhali’s capabilities include autonomous execution of routines for mental health promotion and psychological testing. The software platform enables therapist-directed interventions, allowing the robot to convey emotional gestures through joint and head movements and simulate various facial expressions for more engaging interactions. Finally, with the robot fully operational, an initial behavioral experiment was conducted to validate Qhali’s capability to deliver telepsychological interventions. The findings from this preliminary study indicate that participants reported enhancements in their emotional well-being, along with positive outcomes in their perception of the psychological intervention conducted with the humanoid robot.

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Section: Testing the Qhali Robot In A Controlled Environmentmentioning

confidence: 99%

Qhali: A Humanoid Robot for Assisting in Mental Health Treatment

Pérez-Zuñiga,

Arce,

Gibaja

et al. 2024

Sensors

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“…GANs aim to do implicit density estimation of the underlying distribution through the interplay of a generator that tries to produce samples that are representative of the data, and a discriminator that strengthens the generator by classifying samples as real (from the distribution) or fake (not from the distribution). Multiple gesture generation approaches added an adversarial objective as a term in a composite loss function, which increased the range of gesture motion although still deterministic for a given audio input [SB18, GBK*19, FNM20, YCL*20, ALNM20, RGP21, WLII21b, WLII21a, ZRMOL22, HES*22]. We discuss some notable examples below.…”

Section: Data‐driven Approachesmentioning

confidence: 99%

A Comprehensive Review of Data‐Driven Co‐Speech Gesture Generation

Nyatsanga

Kucherenko²,

Ahuja³

et al. 2023

Computer Graphics Forum

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Gestures that accompany speech are an essential part of natural and efficient embodied human communication. The automatic generation of such co‐speech gestures is a long‐standing problem in computer animation and is considered an enabling technology for creating believable characters in film, games, and virtual social spaces, as well as for interaction with social robots. The problem is made challenging by the idiosyncratic and non‐periodic nature of human co‐speech gesture motion, and by the great diversity of communicative functions that gestures encompass. The field of gesture generation has seen surging interest in the last few years, owing to the emergence of more and larger datasets of human gesture motion, combined with strides in deep‐learning‐based generative models that benefit from the growing availability of data. This review article summarizes co‐speech gesture generation research, with a particular focus on deep generative models. First, we articulate the theory describing human gesticulation and how it complements speech. Next, we briefly discuss rule‐based and classical statistical gesture synthesis, before delving into deep learning approaches. We employ the choice of input modalities as an organizing principle, examining systems that generate gestures from audio, text and non‐linguistic input. Concurrent with the exposition of deep learning approaches, we chronicle the evolution of the related training data sets in terms of size, diversity, motion quality, and collection method (e.g., optical motion capture or pose estimation from video). Finally, we identify key research challenges in gesture generation, including data availability and quality; producing human‐like motion; grounding the gesture in the co‐occurring speech in interaction with other speakers, and in the environment; performing gesture evaluation; and integration of gesture synthesis into applications. We highlight recent approaches to tackling the various key challenges, as well as the limitations of these approaches, and point toward areas of future development.

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“…Current literature suggests that movements performed by robots are able to influence attitudes and perceptions toward them [ 2 , 6 , 7 ]. It has been found that robotic bodily expressions improve the understanding of affect (i.e., emotions and moods attributed to robots) [ 8 ], enhance the perception of trustworthiness [ 2 , 9 ], and awake empathetic responses toward robots [ 7 ]. Moreover, a combination of robotic speech and movements can increase feelings of familiarity [ 7 ] and foster human-like interaction [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Non Linear Control System for Humanoid Robot to Perform Body Language Movements

Gomez-Quispe

Pérez-Zúñiga

Arce

et al. 2023

Sensors

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In social robotics, especially with regard to direct interactions between robots and humans, the robotic movements of the body, arms and head must make an adequate displacement to guarantee an adequate interaction, both from a functional and social point of view. To achieve this, the use of closed-loop control techniques that consider the complex nonlinear dynamics and disturbances inherent in these systems is required. In this paper, an implementation of a nonlinear controller for the tracking of trajectories and a profile of speeds that execute the movements of the arms and head of a humanoid robot based on the mathematical model is proposed. First, the design and implementation of the arms and head are initially presented, then the mathematical model via kinematic and dynamic analysis was performed. With the above, the design of nonlinear controllers such as nonlinear proportional derivative control with gravity compensation, Backstepping control, Sliding Mode control and the application of each of them to the robotic system are presented. A comparative analysis based on a frequency analysis, the efficiency in polynomial trajectories and the implementation requirements allowed selecting the non-linear Backstepping control technique to be implemented. Then, for the implementation, a centralized control architecture is considered, which uses a central microcontroller in the external loop and an internal microcontroller (as internal loop) for each of the actuators. With the above, the selected controller was validated through experiments performed in real time on the implemented humanoid robot, demonstrating proper path tracking of established trajectories for performing body language movements.

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Modeling and evaluating beat gestures for social robots

Cited by 10 publications

References 34 publications

Qhali: A Humanoid Robot for Assisting in Mental Health Treatment

Qhali: A Humanoid Robot for Assisting in Mental Health Treatment

A Comprehensive Review of Data‐Driven Co‐Speech Gesture Generation

Non Linear Control System for Humanoid Robot to Perform Body Language Movements

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