Qhali: A Humanoid Robot for Assisting in Mental Health Treatment.

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.

Older adults' communication with an interactive humanoid robot : Expectations and experiences of older adults in verbal and nonverbal communication with a socially interactive humanoid robot: a mixed methods design in Germany.

BACKGROUND AND OBJECTIVE: One possible approach to counter singularization and loneliness of older adults is the development and implementation of socially interactive robots. Little is known about the expectations and experiences of older adults with socially interactive humanoid robots. MATERIAL AND METHODS: In a mixed-methods design study, user expectations before interaction and the experience and evaluation of verbal and non-verbal communication after interaction with a robot were assessed. Semi-structured interviews were conducted after the interaction. RESULTS: The majority of older adults expected verbal communication. After the interaction the evaluation of the quality of verbal communication differed. Participants did not expect any form of nonverbal communication. Nonverbal communication was highlighted as particularly positive. Gestures, facial expressions, and body movements were described as confidence building. CONCLUSION: The robot's ability to communicate nonverbally might positively influence older adults' experience of communication with the robot. In the development of socially interactive robots non-verbal communication should be given more consideration in order to contribute to successful human-robot interaction.

Zero Moment Line-Universal Stability Parameter for Multi-Contact Systems in Three Dimensions.

The widely used stability parameter, the zero moment point (ZMP), which is usually defined on the ground, is redefined, in this paper, in two different ways to acquire a more general form that allows its application to systems that are not supported only on the ground, and therefore, their support polygon does not extend only on the floor. This way it allows to determine the stability of humanoid and other floating-based robots that are interacting with the environment at arbitrary heights. In the first redefinition, the ZMP is represented as a line containing all possible ZMPs, called the zero moment line (ZML), while in the second redefinition, the ZMP is represented as the ZMP angle, i.e., the angle between the ZML and the vertical line, passing through the center of mass (COM) of the investigated system. The first redefinition is useful in situations when the external forces and their acting locations are known, while the second redefinition can be applied in situations when the COM of the system under study is known and can be tracked. The first redefinition of the ZMP is also applied to two different measurements performed with two force plates, two force sensors, and the Optitrack system. In the first measurement, a subject stands up from a bench and sits down while being pulled by its hands, while in the second measurement, two subjects stand still, hold on to two double handles, and lean backward. In both cases, the stability of the subjects involved in the measurements is investigated and discussed.

Children's Pain During IV Induction: A Randomized-Controlled Trial With the MEDi® Robot.

OBJECTIVE: This study examined the impact of a humanoid robot (MEDi®) programmed to teach deep breathing as a coping strategy, on children's pain and fear as primary and secondary outcomes, respectively, during intravenous (IV) line placement. The completion of IV induction was also examined as an exploratory outcome. METHODS: In this randomized controlled, two-armed trial, 137 children (4-12 years) were recruited in Short Stay Surgery at a tertiary pediatric hospital. Patients were randomly assigned to standard care (SC) with Ametop© only (N = 60) or SC and robot-facilitated intervention (N = 59) before induction. Pain and fear before, during, and after IV insertion were rated by patients and observers. RESULTS: No significant differences were found between groups and there were no changes over time for pain or fear (ps > .05). Exploratory analyses show that patients in the MEDi® group were 5.04 times more likely to complete IV induction, compared to SC, Fisher's exact test: X2 (1) = 4.85, p = .04, φc = 0.22, odds ratio = 5.04, 95% CI [1.06, 24.00]. CONCLUSION: This study was the first to examine children's IV induction experience when provided MEDi® support. Reasons for nonsignificance, limitations, and research suggestions were made.

Response of ChatGPT for Humanoid Robots Role in Improving Healthcare and Patient Outcomes.

Humanoid robotics is characterized by constant developments, which are supported by several research facilities across the world. Humanoid robots are used in many different industries. In this setting, this letter, written by people, makes use of ChatGPT answers to examine how humanoid robots might be used in the medical industry, particularly in light of the COVID-19 pandemic and in future. Although humanoid robots can help with certain jobs, it is important to recognize the indispensable importance of human healthcare professionals who have knowledge, empathy, and the capacity for critical judgment. Although humanoid robots can complement healthcare initiatives, they shouldn't be viewed as a full-fledged replacement for human care.

Bimodal Tactile Sensor without Signal Fusion for User-Interactive Applications.

Tactile sensors with multimode sensing ability are cornerstones of artificial skin for applications in humanoid robotics and smart prosthetics. However, the intuitive and interference-free reading of multiple tactile signals without involving complex algorithms and calculations remains a challenge. Herein a pressure-temperature bimodal tactile sensor without any interference is demonstrated by combining the fundamentally different sensing mechanisms of optics and electronics, enabling the simultaneous and independent sensing of pressure and temperature with the elimination of signal separation algorithms and calculations. The bimodal sensor comprises a mechanoluminescent hybrid of ZnS-CaZnOS and a poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) thermoresistant material, endowing the unambiguous transduction of pressure and temperature into optical and electrical signals, respectively. This device exhibits the highest temperature sensitivity of -0.6% °C-1 in the range of 21-60 °C and visual sensing of the applied forces at a low limitation of 2 N. The interference-free and light-emitting characteristics of this device permit user-interactive applications in robotics for encrypted communication as well as temperature and pressure monitoring, along with wireless signal transmission. This work provides an unexplored solution to signal interference of multimodal tactile sensors, which can be extended to other multifunctional sensing devices.

Towards understanding and synthesis of contact-rich anthropomorphic motions through interactive cyber-physical human.

This article presents perspective on the research challenge of understanding and synthesizing anthropomorphic whole-body contact motions through a platform called "interactive cyber-physical human (iCPH)" for data collection and augmentation. The iCPH platform combines humanoid robots as "physical twins" of human and "digital twins" that simulates humans and robots in cyber-space. Several critical research topics are introduced to address this challenge by leveraging the advanced model-based analysis together with data-driven learning to exploit collected data from the integrated platform of iCPH. Definition of general description is identified as the first topic as a common basis of contact motions compatible to both humans and humanoids. Then, we set continual learning of a feasible contact motion network as the second challenge by benefiting from model-based approach and machine learning bridged by the efficient analytical gradient computation developed by the author and his collaborators. The final target is to establish a high-level symbolic system allowing automatic understanding and generation of contact motions in unexperienced environments. The proposed approaches are still under investigation, and the author expects that this article triggers discussions and further collaborations from different research communities, including robotics, artificial intelligence, neuroscience, and biomechanics.

Service humanoid robotics: a novel interactive system based on bionic-companionship framework.

At present, industrial robotics focuses more on motion control and vision, whereas humanoid service robotics (HSRs) are increasingly being investigated and researched in the field of speech interaction. The problem and quality of human-robot interaction (HRI) has become a widely debated topic in academia. Especially when HSRs are applied in the hospitality industry, some researchers believe that the current HRI model is not well adapted to the complex social environment. HSRs generally lack the ability to accurately recognize human intentions and understand social scenarios. This study proposes a novel interactive framework suitable for HSRs. The proposed framework is grounded on the novel integration of Trevarthen's (2001) companionship theory and neural image captioning (NIC) generation algorithm. By integrating image-to-natural interactivity generation and communicating with the environment to better interact with the stakeholder, thereby changing from interaction to a bionic-companionship. Compared to previous research a novel interactive system is developed based on the bionic-companionship framework. The humanoid service robot was integrated with the system to conduct preliminary tests. The results show that the interactive system based on the bionic-companionship framework can help the service humanoid robot to effectively respond to changes in the interactive environment, for example give different responses to the same character in different scenes.

Parallel and hierarchical neural mechanisms for adaptive and predictive behavioral control.

Our brain can be recognized as a network of largely hierarchically organized neural circuits that operate to control specific functions, but when acting in parallel, enable the performance of complex and simultaneous behaviors. Indeed, many of our daily actions require concurrent information processing in sensorimotor, associative, and limbic circuits that are dynamically and hierarchically modulated by sensory information and previous learning. This organization of information processing in biological organisms has served as a major inspiration for artificial intelligence and has helped to create in silico systems capable of matching or even outperforming humans in several specific tasks, including visual recognition and strategy-based games. However, the development of human-like robots that are able to move as quickly as humans and respond flexibly in various situations remains a major challenge and indicates an area where further use of parallel and hierarchical architectures may hold promise. In this article we review several important neural and behavioral mechanisms organizing hierarchical and predictive processing for the acquisition and realization of flexible behavioral control. Then, inspired by the organizational features of brain circuits, we introduce a multi-timescale parallel and hierarchical learning framework for the realization of versatile and agile movement in humanoid robots.

Learning to teleoperate an upper-limb assistive humanoid robot for bimanual daily-living tasks.

Objective.Bimanual humanoid platforms for home assistance are nowadays available, both as academic prototypes and commercially. Although they are usually thought of as daily helpers for non-disabled users, their ability to move around, together with their dexterity, makes them ideal assistive devices for upper-limb disabled persons, too. Indeed, teleoperating a bimanual robotic platform via muscle activation could revolutionize the way stroke survivors, amputees and patients with spinal injuries solve their daily home chores. Moreover, with respect to direct prosthetic control, teleoperation has the advantage of freeing the user from the burden of the prosthesis itself, overpassing several limitations regarding size, weight, or integration, and thus enables a much higher level of functionality.Approach.In this study, nine participants, two of whom suffer from severe upper-limb disabilities, teleoperated a humanoid assistive platform, performing complex bimanual tasks requiring high precision and bilateral arm/hand coordination, simulating home/office chores. A wearable body posture tracker was used for position control of the robotic torso and arms, while interactive machine learning applied to electromyography of the forearms helped the robot to build an increasingly accurate model of the participant's intent over time.Main results.All participants, irrespective of their disability, were uniformly able to perform the demanded tasks. Completion times, subjective evaluation scores, as well as energy- and time- efficiency show improvement over time on short and long term.Significance.This is the first time a hybrid setup, involving myoeletric and inertial measurements, is used by disabled people to teleoperate a bimanual humanoid robot. The proposed setup, taking advantage of interactive machine learning, is simple, non-invasive, and offers a new assistive solution for disabled people in their home environment. Additionnally, it has the potential of being used in several other applications in which fine humanoid robot control is required.

MaskUKF: An Instance Segmentation Aided Unscented Kalman Filter for 6D Object Pose and Velocity Tracking.

Tracking the 6D pose and velocity of objects represents a fundamental requirement for modern robotics manipulation tasks. This paper proposes a 6D object pose tracking algorithm, called MaskUKF, that combines deep object segmentation networks and depth information with a serial Unscented Kalman Filter to track the pose and the velocity of an object in real-time. MaskUKF achieves and in most cases surpasses state-of-the-art performance on the YCB-Video pose estimation benchmark without the need for expensive ground truth pose annotations at training time. Closed loop control experiments on the iCub humanoid platform in simulation show that joint pose and velocity tracking helps achieving higher precision and reliability than with one-shot deep pose estimation networks. A video of the experiments is available as Supplementary Material.

A Differentiable Extended Kalman Filter for Object Tracking Under Sliding Regime.

Tactile sensing represents a valuable source of information in robotics for perception of the state of objects and their properties. Modern soft tactile sensors allow perceiving orthogonal forces and, in some cases, relative motions along the surface of the object. Detecting and measuring this kind of lateral motion is fundamental to react to possibly uncontrolled slipping and sliding of the object being manipulated. Object slip detection and prediction have been extensively studied in the robotic community leading to solutions with good accuracy and suitable for closed-loop grip stabilization. However, algorithms for object perception, such as in-hand object pose estimation and tracking algorithms, often assume no relative motion between the object and the hand and rarely consider the problem of tracking the pose of the object subjected to slipping and sliding motions. In this work, we propose a differentiable Extended Kalman filter that can be trained to track the position and the velocity of an object under translational sliding regime from tactile observations alone. Experiments with several objects, carried out on the iCub humanoid robot platform, show that the proposed approach allows achieving an average position tracking error in the order of 0.6 cm, and that the provided estimate of the object state can be used to take control decisions using tactile feedback alone. A video of the experiments is available as Supplementary Material.

Event-Based Eccentric Motion Detection Exploiting Time Difference Encoding.

Attentional selectivity tends to follow events considered as interesting stimuli. Indeed, the motion of visual stimuli present in the environment attract our attention and allow us to react and interact with our surroundings. Extracting relevant motion information from the environment presents a challenge with regards to the high information content of the visual input. In this work we propose a novel integration between an eccentric down-sampling of the visual field, taking inspiration from the varying size of receptive fields (RFs) in the mammalian retina, and the Spiking Elementary Motion Detector (sEMD) model. We characterize the system functionality with simulated data and real world data collected with bio-inspired event driven cameras, successfully implementing motion detection along the four cardinal directions and diagonally.

Robot DE NIRO: A Human-Centered, Autonomous, Mobile Research Platform for Cognitively-Enhanced Manipulation.

We introduce Robot DE NIRO, an autonomous, collaborative, humanoid robot for mobile manipulation. We built DE NIRO to perform a wide variety of manipulation behaviors, with a focus on pick-and-place tasks. DE NIRO is designed to be used in a domestic environment, especially in support of caregivers working with the elderly. Given this design focus, DE NIRO can interact naturally, reliably, and safely with humans, autonomously navigate through environments on command, intelligently retrieve or move target objects, and avoid collisions efficiently. We describe DE NIRO's hardware and software, including an extensive vision sensor suite of 2D and 3D LIDARs, a depth camera, and a 360-degree camera rig; two types of custom grippers; and a custom-built exoskeleton called DE VITO. We demonstrate DE NIRO's manipulation capabilities in three illustrative challenges: First, we have DE NIRO perform a fetch-an-object challenge. Next, we add more cognition to DE NIRO's object recognition and grasping abilities, confronting it with small objects of unknown shape. Finally, we extend DE NIRO's capabilities into dual-arm manipulation of larger objects. We put particular emphasis on the features that enable DE NIRO to interact safely and naturally with humans. Our contribution is in sharing how a humanoid robot with complex capabilities can be designed and built quickly with off-the-shelf hardware and open-source software. Supplementary Material including our code, a documentation, videos and the CAD models of several hardware parts are openly available at https://www.imperial.ac.uk/robot-intelligence/software/.

Iconic Gestures for Robot Avatars, Recognition and Integration with Speech.

Co-verbal gestures are an important part of human communication, improving its efficiency and efficacy for information conveyance. One possible means by which such multi-modal communication might be realized remotely is through the use of a tele-operated humanoid robot avatar. Such avatars have been previously shown to enhance social presence and operator salience. We present a motion tracking based tele-operation system for the NAO robot platform that allows direct transmission of speech and gestures produced by the operator. To assess the capabilities of this system for transmitting multi-modal communication, we have conducted a user study that investigated if robot-produced iconic gestures are comprehensible, and are integrated with speech. Robot performed gesture outcomes were compared directly to those for gestures produced by a human actor, using a within participant experimental design. We show that iconic gestures produced by a tele-operated robot are understood by participants when presented alone, almost as well as when produced by a human. More importantly, we show that gestures are integrated with speech when presented as part of a multi-modal communication equally well for human and robot performances.

Event-driven visual attention for the humanoid robot iCub.

Fast reaction to sudden and potentially interesting stimuli is a crucial feature for safe and reliable interaction with the environment. Here we present a biologically inspired attention system developed for the humanoid robot iCub. It is based on input from unconventional event-driven vision sensors and an efficient computational method. The resulting system shows low-latency and fast determination of the location of the focus of attention. The performance is benchmarked against an instance of the state of the art in robotics artificial attention system used in robotics. Results show that the proposed system is two orders of magnitude faster that the benchmark in selecting a new stimulus to attend.