Investigating the performance of soft robotic adaptive feet with longitudinal and transverse arches.

Biped robots usually adopt feet with a rigid structure that simplifies walking on flat grounds and yet hinders ground adaptation in unstructured environments, thus jeopardizing stability. We recently explored in the SoftFoot the idea of adapting a robotic foot to ground irregularities along the sagittal plane. Building on the previous results, we propose in this paper a novel robotic foot able to adapt both in the sagittal and frontal planes, similarly to the human foot. It features five parallel modules with intrinsic longitudinal adaptability that can be combined in many possible designs through optional rigid or elastic connections. By following a methodological design approach, we narrow down the design space to five candidate foot designs and implement them on a modular system. Prototypes are tested experimentally via controlled application of force, through a robotic arm, onto a sensorized plate endowed with different obstacles. Their performance is compared, using also a rigid foot and the previous SoftFoot as a baseline. Analysis of footprint stability shows that the introduction of the transverse arch, by elastically connecting the five parallel modules, is advantageous for obstacle negotiation, especially when obstacles are located under the forefoot. In addition to biped robots' locomotion, this finding might also benefit lower-limb prostheses design.

Is it possible for people to develop a sense of empathy toward humanoid robots and establish meaningful relationships with them?

Introduction: Empathy can be described as the ability to adopt another person's perspective and comprehend, feel, share, and respond to their emotional experiences. Empathy plays an important role in these relationships and is constructed in human-robot interaction (HRI). This systematic review focuses on studies investigating human empathy toward robots. We intend to define empathy as the cognitive capacity of humans to perceive robots as equipped with emotional and psychological states. Methods: We conducted a systematic search of peer-reviewed articles using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We searched Scopus, PubMed, Web of Science, and Embase databases. All articles were reviewed based on the titles, abstracts, and full texts by two investigators (EM and CS) who independently performed data collection. The researchers read the full-text articles deemed suitable for the study, and in cases of disagreement regarding the inclusion and exclusion criteria, the final decision was made by a third researcher (VLB). Results: The electronic search identified 484 articles. After reading the full texts of the selected publications and applying the predefined inclusion criteria, we selected 11 articles that met our inclusion criteria. Robots that could identify and respond appropriately to the emotional states of humans seemed to evoke empathy. In addition, empathy tended to grow more when the robots exhibited anthropomorphic traits. Discussion: Humanoid robots can be programmed to understand and react to human emotions and simulate empathetic responses; however, they are not endowed with the same innate capacity for empathy as humans.

Tools and methods to study and replicate experiments addressing human social cognition in interactive scenarios.

In the last decade, scientists investigating human social cognition have started bringing traditional laboratory paradigms more "into the wild" to examine how socio-cognitive mechanisms of the human brain work in real-life settings. As this implies transferring 2D observational paradigms to 3D interactive environments, there is a risk of compromising experimental control. In this context, we propose a methodological approach which uses humanoid robots as proxies of social interaction partners and embeds them in experimental protocols that adapt classical paradigms of cognitive psychology to interactive scenarios. This allows for a relatively high degree of "naturalness" of interaction and excellent experimental control at the same time. Here, we present two case studies where our methods and tools were applied and replicated across two different laboratories, namely the Italian Institute of Technology in Genova (Italy) and the Agency for Science, Technology and Research in Singapore. In the first case study, we present a replication of an interactive version of a gaze-cueing paradigm reported in Kompatsiari et al. (J Exp Psychol Gen 151(1):121-136, 2022). The second case study presents a replication of a "shared experience" paradigm reported in Marchesi et al. (Technol Mind Behav 3(3):11, 2022). As both studies replicate results across labs and different cultures, we argue that our methods allow for reliable and replicable setups, even though the protocols are complex and involve social interaction. We conclude that our approach can be of benefit to the research field of social cognition and grant higher replicability, for example, in cross-cultural comparisons of social cognition mechanisms.

Robots as addressable non-persons: an analysis of categorial work at the boundaries of the social world.

Prompted by the material turn in the social sciences and the development of novel interaction technologies, lively debates in social theory have arisen regarding the agency of non-human entities. While these debates primarily involve exchanging theoretical arguments against the background of different theoretical positions, ethnomethodological membership categorization analysis (MCA) provides an empirical approach to questions of non-human agency. The article discusses the debate on non-human agency, demonstrates how MCA can be used to investigate categorial work at the boundaries of the social, and presents the example of an encounter between two museum visitors and a humanoid robot to show how the robot is categorized in a specific way as an 'addressable non-person.' In this way, it becomes clear that social-theoretical debates and empirically oriented MCA can mutually inspire each other and how the 'basic categorization apparatus' addresses new alterities.

Whole-Body Dynamics for Humanoid Robot Fall Protection Trajectory Generation with Wall Support.

When humanoid robots work in human environments, they are prone to falling. However, when there are objects around that can be utilized, humanoid robots can leverage them to achieve balance. To address this issue, this paper established the state equation of a robot using a variable height-inverted pendulum model and implemented online trajectory optimization using model predictive control. For the arms' optimal joint angles during movement, this paper took the distributed polygon method to calculate the arm postures. To ensure that the robot reached the target position smoothly and rapidly during its motion, this paper adopts a whole-body motion control approach, establishing a cost function for multi-objective constraints on the robot's movement. These constraints include whole-body dynamics, center of mass constraints, arm's end effector constraints, friction constraints, and center of pressure constraints. In the simulation, four sets of methods were compared, and the experimental results indicate that compared to free fall motion, adopting the method proposed in this paper reduces the maximum acceleration of the robot when it touches the wall to 69.1 m/s2, effectively reducing the impact force upon landing. Finally, in the actual experiment, we positioned the robot 0.85 m away from the wall and applied a forward pushing force. We observed that the robot could stably land on the wall, and the impact force was within the range acceptable to the robot, confirming the practical effectiveness of the proposed method.

Walking position commanded NAO robot using nonlinear disturbance observer-based fixed-time terminal sliding mode.

The walking stability of a humanoid robot is a fundamental problem due to the complex nonlinear dynamic model of the robot's legs. This work introduces the performance tracking control for the humanoid NAO robot by using a Nonlinear Disturbance Observer (NDO)-based Fixed-time Terminal Sliding Mode (FTSM). The influence of uncertain external disturbance is considered while implementing the control strategy to improve the walking motion of the NAO robot. An NDO is adapted to estimate the uncertainties and external disturbances. A novel FTSM surface is proposed to drive the tracking errors to zero in fixed-time. The designed NDO-based FTSM control law achieves robustness while reducing the chattering phenomenon. The Lyapunov's stability theory is used to establish the fixed-time stability of the sliding surface and system states under the proposed control method. To validate the performance of the proposed NDO-based FTSM control, a real-time experiment was conducted on a humanoid NAO robot to demonstrate the improved tracking performance in the presence of the uncertain perturbation effect. The effectiveness of the proposed controller design is validated on a flat, upward inclined surface, and compared to another controller.

Curriculum-based humanoid robot identification using large-scale human motion database.

Identifying an accurate dynamics model remains challenging for humanoid robots. The difficulty is mainly due to the following two points. First, a good initial model is required to evaluate the feasibility of motions for data acquisition. Second, a highly nonlinear optimization problem needs to be solved to design movements to acquire the identification data. To cope with the first point, in this paper, we propose a curriculum of identification to gradually learn an accurate dynamics model from an unreliable initial model. For the second point, we propose using a large-scale human motion database to efficiently design the humanoid movements for the parameter identification. The contribution of our study is developing a humanoid identification method that does not require the good initial model and does not need to solve the highly nonlinear optimization problem. We showed that our curriculum-based approach was able to more efficiently identify humanoid model parameters than a method that just randomly picked reference motions for identification. We evaluated our proposed method in a simulation experiment and demonstrated that our curriculum was led to obtain a wide variety of motion data for efficient parameter estimation. Consequently, our approach successfully identified an accurate model of an 18-DoF, simulated upper-body humanoid robot.

Control and evaluation of a humanoid robot with rolling contact joints on its lower body.

In this paper, we introduce a new teen-sized humanoid platform dubbed DRACO 3, custom-built by Apptronik and altered for practical use by the Human Centered Robotics Laboratory at The University of Texas at Austin. The form factor of DRACO 3 is such that it can operate safely in human environments while reaching objects at human heights. To approximate the range of motion of humans, this robot features proximal actuation and mechanical artifacts to provide a high range of hip, knee, and ankle motions. In particular, rolling contact mechanisms on the lower body are incorporated using a proximal actuation principle to provide an extensive vertical pose workspace. To enable DRACO 3 to perform dexterous tasks while dealing with these complex transmissions, we introduce a novel whole-body controller (WBC) incorporating internal constraints to model the rolling motion behavior. In addition, details of our WBC for DRACO 3 are presented with an emphasis on practical points for hardware implementation. We perform a design analysis of DRACO 3, as well as empirical evaluations under the lens of the Centroidal Inertia Isotropy (CII) design metric. Lastly, we experimentally validate our design and controller by testing center of mass (CoM) balancing, one-leg balancing, and stepping-in-place behaviors.

Joint Reconfiguration after Failure for Performing Emblematic Gestures in Humanoid Receptionist Robot.

This study proposed a strategy for a quick fault recovery response when an actuator failure problem occurred while a humanoid robot with 7-DOF anthropomorphic arms was performing a task with upper body motion. The objective of this study was to develop an algorithm for joint reconfiguration of the receptionist robot called Namo so that the robot can still perform a set of emblematic gestures if an actuator fails or is damaged. We proposed a gesture similarity measurement to be used as an objective function and used bio-inspired artificial intelligence methods, including a genetic algorithm, a bacteria foraging optimization algorithm, and an artificial bee colony, to determine good solutions for joint reconfiguration. When an actuator fails, the failed joint will be locked at the average angle calculated from all emblematic gestures. We used grid search to determine suitable parameter sets for each method before making a comparison of their performance. The results showed that bio-inspired artificial intelligence methods could successfully suggest reconfigured gestures after joint motor failure within 1 s. After 100 repetitions, BFOA and ABC returned the best-reconfigured gestures; there was no statistical difference. However, ABC yielded more reliable reconfigured gestures; there was significantly less interquartile range among the results than BFOA. The joint reconfiguration method was demonstrated for all possible joint failure conditions. The results showed that the proposed method could determine good reconfigured gestures under given time constraints; hence, it could be used for joint failure recovery in real applications.

Biomimetic Approaches for Human Arm Motion Generation: Literature Review and Future Directions.

In recent years, numerous studies have been conducted to analyze how humans subconsciously optimize various performance criteria while performing a particular task, which has led to the development of robots that are capable of performing tasks with a similar level of efficiency as humans. The complexity of the human body has led researchers to create a framework for robot motion planning to recreate those motions in robotic systems using various redundancy resolution methods. This study conducts a thorough analysis of the relevant literature to provide a detailed exploration of the different redundancy resolution methodologies used in motion generation for mimicking human motion. The studies are investigated and categorized according to the study methodology and various redundancy resolution methods. An examination of the literature revealed a strong trend toward formulating intrinsic strategies that govern human movement through machine learning and artificial intelligence. Subsequently, the paper critically evaluates the existing approaches and highlights their limitations. It also identifies the potential research areas that hold promise for future investigations.

Artificial scaffolding: Augmenting social cognition by means of robot technology.

The concept of scaffolding refers to the support that the environment provides in the acquisition and consolidation of new abilities. Technological advancements allow for support in the acquisition of cognitive capabilities, such as second language acquisition using simple smartphone applications There is, however, one domain of cognition that has been scarcely addressed in the context of technologically assisted scaffolding: social cognition. We explored the possibility of supporting the acquisition of social competencies of a group of children with autism spectrum disorder engaged in a rehabilitation program (age = 5.8 ± 1.14, 10 females, 33 males) by designing two robot-assisted training protocols tailored to Theory of Mind competencies. One protocol was performed with a humanoid robot and the other (control) with a non-anthropomorphic robot. We analyzed changes in NEPSY-II scores before and after the training using mixed effects models. Our results showed that activities with the humanoid significantly improved NEPSY-II scores on the ToM scale. We claim that the motor repertoire of humanoids makes them ideal platforms for artificial scaffolding of social skills in individuals with autism, as they can evoke similar social mechanisms to those elicited in human-human interaction, without providing the same social pressure that another human might exert.

Turn-Taking Mechanisms in Imitative Interaction: Robotic Social Interaction Based on the Free Energy Principle.

This study explains how the leader-follower relationship and turn-taking could develop in a dyadic imitative interaction by conducting robotic simulation experiments based on the free energy principle. Our prior study showed that introducing a parameter during the model training phase can determine leader and follower roles for subsequent imitative interactions. The parameter is defined as w, the so-called meta-prior, and is a weighting factor used to regulate the complexity term versus the accuracy term when minimizing the free energy. This can be read as sensory attenuation, in which the robot's prior beliefs about action are less sensitive to sensory evidence. The current extended study examines the possibility that the leader-follower relationship shifts depending on changes in w during the interaction phase. We identified a phase space structure with three distinct types of behavioral coordination using comprehensive simulation experiments with sweeps of w of both robots during the interaction. Ignoring behavior in which the robots follow their own intention was observed in the region in which both ws were set to large values. One robot leading, followed by the other robot was observed when one w was set larger and the other was set smaller. Spontaneous, random turn-taking between the leader and the follower was observed when both ws were set at smaller or intermediate values. Finally, we examined a case of slowly oscillating w in anti-phase between the two agents during the interaction. The simulation experiment resulted in turn-taking in which the leader-follower relationship switched during determined sequences, accompanied by periodic shifts of ws. An analysis using transfer entropy found that the direction of information flow between the two agents also shifted along with turn-taking. Herein, we discuss qualitative differences between random/spontaneous turn-taking and agreed-upon sequential turn-taking by reviewing both synthetic and empirical studies.

Socially Assistive Humanoid Robots: Effects on Depression and Health-Related Quality of Life among Low-Income, Socially Isolated Older Adults in South Korea.

Using a mixed-method study design, we examined the effects of a socially assistive humanoid robot (SAHR), called Hyodol, on depressive symptoms and health-related quality of life (HRQOL) of low-income, socially isolated older adults (N = 180). Quantitative outcomes were assessed at baseline (before Hyodol deployment) and at 3 and 6 months after baseline. Results showed reduced depressive symptoms and improved HRQOL at 3 months; however, these positive effects did not extend to 6 months. Ten focus group participants perceived Hyodol to be a valuable companion especially during the COVID outbreak. These results suggest that while Hyodol may have provided companionship for some low-income, socially isolated older adults during home confinement, its effects on depression and HRQOL were limited. Further research is needed to assess long-term effects of SAHRs as appropriate tools for reducing social isolation and improving behavioral health among community-dwelling older adults.

A randomized controlled trial for evaluating the impact of integrating a computerized clinical decision support system and a socially assistive humanoid robot into grand rounds during pre/post-operative care.

Although clinical decision support systems (CDSSs) are increasingly emphasized as one of the possible levers for improving care, they are still not widely used due to different barriers, such as doubts about systems' performance, their complexity and poor design, practitioners' lack of time to use them, poor computer skills, reluctance to use them in front of patients, and deficient integration into existing workflows. While several studies on CDSS exist, there is a need for additional high-quality studies using large samples and examining the differences between outcomes following a decision based on CDSS support and those following decisions without this kind of information. Even less is known about the effectiveness of a CDSS that is delivered during a grand round routine and with the help of socially assistive humanoid robots (SAHRs). In this study, 200 patients will be randomized into a Control Group (i.e. standard care) and an Intervention Group (i.e. standard care and novel CDSS delivered via a SAHR). Health care quality and Quality of Life measures will be compared between the two groups. Additionally, approximately 22 clinicians, who are also active researchers at the University Clinical Center Maribor, will evaluate the acceptability and clinical usability of the system. The results of the proposed study will provide high-quality evidence on the effectiveness of CDSS systems and SAHR in the grand round routine.

Design and Performance Analysis of LARMbot Torso V1.

In this paper, laboratory experiments of LARMbot torso V1 are reported in the third mode, thereby providing a testing characterization. Sensors were used to measure parameters including the contact force between the shoulder and cables, linear acceleration, angles of the torso body, and power consumption. The results showed that the LARMbot torso V1 can bend successfully to the desired angles, and that it is able to complete a full motion smoothly. The LARMbot torso V1 can mimic human-like motiaons. Based on our analysis of the test results, improvements are suggested, and new designs are considered.

Optimization of Dynamic Sit-to-Stand Trajectories to Assess Whole-Body Motion Performance of the Humanoid Robot REEM-C.

To enable the application of humanoid robots outside of laboratory environments, the biped must meet certain requirements. These include, in particular, coping with dynamic motions such as climbing stairs or ramps or walking over irregular terrain. Sit-to-stand transitions also belong to this category. In addition to their actual application such as getting out of vehicles or standing up after sitting, for example, at a table, these motions also provide benefits in terms of performance assessment. Therefore, they have long been used as a sports medical and geriatric assessment for humans. Here, we develop optimized sit-to-stand trajectories using optimal control, which are characterized by their dynamic and humanlike nature. We implement these motions on the humanoid robot REEM-C. Based on the obtained sensor data, we present a unified benchmarking procedure based on two different experimental protocols. These protocols are characterized by their increasing level of difficulty for quantifying different aspects of lower limb performance. We report performance results obtained by REEM-C using two categories of indicators: primary, scenario-specific indicators that assess overall performance (chair height and ankle-to-chair distance) and subsidiary, general indicators that further describe performance. The latter provide a more detailed analysis of the applied motion and are based on metrics such as the angular momentum, zero moment point, capture point, or foot placement estimator. In the process, we identify performance deficiencies of the robot based on the collected data. Thus, this work is an important step toward a unified quantification of bipedal performance in the execution of humanlike and dynamically demanding motions.

Facing the FACS-Using AI to Evaluate and Control Facial Action Units in Humanoid Robot Face Development.

This paper presents a new approach for evaluating and controlling expressive humanoid robotic faces using open-source computer vision and machine learning methods. Existing research in Human-Robot Interaction lacks flexible and simple tools that are scalable for evaluating and controlling various robotic faces; thus, our goal is to demonstrate the use of readily available AI-based solutions to support the process. We use a newly developed humanoid robot prototype intended for medical training applications as a case example. The approach automatically captures the robot's facial action units through a webcam during random motion, which are components traditionally used to describe facial muscle movements in humans. Instead of manipulating the actuators individually or training the robot to express specific emotions, we propose using action units as a means for controlling the robotic face, which enables a multitude of ways to generate dynamic motion, expressions, and behavior. The range of action units achieved by the robot is thus analyzed to discover its expressive capabilities and limitations and to develop a control model by correlating action units to actuation parameters. Because the approach is not dependent on specific facial attributes or actuation capabilities, it can be used for different designs and continuously inform the development process. In healthcare training applications, our goal is to establish a prerequisite of expressive capabilities of humanoid robots bounded by industrial and medical design constraints. Furthermore, to mediate human interpretation and thus enable decision-making based on observed cognitive, emotional, and expressive cues, our approach aims to find the minimum viable expressive capabilities of the robot without having to optimize for realism. The results from our case example demonstrate the flexibility and efficiency of the presented AI-based solutions to support the development of humanoid facial robots.