Frontiers in Robotics and AI

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EISSN : 2296-9144
Current Publisher: Frontiers Media SA (10.3389)
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Total articles ≅ 871
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, Jonas Hubertus, Sipontina Croce, Günter Schultes, Stefan Seelecke, Gianluca Rizzello
Frontiers in Robotics and AI, Volume 8; doi:10.3389/frobt.2021.695918

Abstract:
The availability of compliant actuators is essential for the development of soft robotic systems. Dielectric elastomers (DEs) represent a class of smart actuators which has gained a significant popularity in soft robotics, due to their unique mix of large deformation (>100%), lightweight, fast response, and low cost. A DE consists of a thin elastomer membrane coated with flexible electrodes on both sides. When a high voltage is applied to the electrodes, the membrane undergoes a controllable mechanical deformation. In order to produce a significant actuation stroke, a DE membrane must be coupled with a mechanical biasing system. Commonly used spring-like bias elements, however, are generally made of rigid materials such as steel, and thus they do not meet the compliance requirements of soft robotic applications. To overcome this issue, in this paper we propose a novel type of compliant mechanism as biasing elements for DE actuators, namely a three-dimensional polymeric dome. When properly designed, such types of mechanisms exhibit a region of negative stiffness in their force-displacement behavior. This feature, in combination with the intrinsic softness of the polymeric material, ensures large actuation strokes as well as compliance compatibility with soft robots. After presenting the novel biasing concept, the overall soft actuator design, manufacturing, and assembly are discussed. Finally, experimental characterization is conducted, and the suitability for soft robotic applications is assessed.
Joshua Hawthorne-Madell, Eric Aaron, Ken Livingston, John H. Jr. Long
Frontiers in Robotics and AI, Volume 8; doi:10.3389/frobt.2021.674823

Abstract:
Given that selection removes genetic variance from evolving populations, thereby reducing exploration opportunities, it is important to find mechanisms that create genetic variation without the disruption of adapted genes and genomes caused by random mutation. Just such an alternative is offered by random epigenetic error, a developmental process that acts on materials and parts expressed by the genome. In this system of embodied computational evolution, simulated within a physics engine, epigenetic error was instantiated in an explicit genotype-to-phenotype map as transcription error at the initiation of gene expression. The hypothesis was that transcription error would create genetic variance by shielding genes from the direct impact of selection, creating, in the process, masquerading genomes. To test this hypothesis, populations of simulated embodied biorobots and their developmental systems were evolved under steady directional selection as equivalent rates of random mutation and random transcriptional error were covaried systematically in an 11 × 11 fully factorial experimental design. In each of the 121 different experimental conditions (unique combinations of mutation and transcription error), the same set of 10 randomly created replicate populations of 60 individuals were evolved. Selection for the improved locomotor behavior of individuals led to increased mean fitness of populations over 100 generations at nearly all levels and combinations of mutation and transcription error. When the effects of both types of error were partitioned statistically, increasing transcription error was shown to increase the final genetic variance of populations, incurring a fitness cost but acting on variance independently and differently from genetic mutation. Thus, random epigenetic errors in development feed back through selection of individuals with masquerading genomes to the population’s genetic variance over generational time. Random developmental processes offer an additional mechanism for exploration by increasing genetic variation in the face of steady, directional selection.
, Leonardo Franco, Martin Tschiersky, , Matteo Bianchi, Antonio Bicchi, Federica Barontini, Manuel Catalano, Giorgio Grioli, Mattia Poggiani, et al.
Frontiers in Robotics and AI, Volume 8; doi:10.3389/frobt.2021.661354

Abstract:
Upper-limb impairments are all-pervasive in Activities of Daily Living (ADLs). As a consequence, people affected by a loss of arm function must endure severe limitations. To compensate for the lack of a functional arm and hand, we developed a wearable system that combines different assistive technologies including sensing, haptics, orthotics and robotics. The result is a device that helps lifting the forearm by means of a passive exoskeleton and improves the grasping ability of the impaired hand by employing a wearable robotic supernumerary finger. A pilot study involving 3 patients, which was conducted to test the capability of the device to assist in performing ADLs, confirmed its usefulness and serves as a first step in the investigation of novel paradigms for robotic assistance.
, Michael Lackner, Sonja Laicher, Rüdiger Neumann, Zoltán Major
Frontiers in Robotics and AI, Volume 8; doi:10.3389/frobt.2021.663158

Abstract:
State-of-the-art Additive Manufacturing processes such as three-dimensional (3D) inkjet printing are capable of producing geometrically complex multi-material components with integrated elastomeric features. Researchers and engineers seeking to exploit these capabilities must handle the complex mechanical behavior of inkjet-printed elastomers and expect a lack of suitable design examples. We address these obstacles using a pneumatic actuator as an application case. First, an inkjet-printable actuator design with elastomeric bellows structures is presented. While soft robotics research has brought forward several examples of inkjet-printed linear and bending bellows actuators, the rotary actuator described here advances into the still unexplored field of additively manufactured pneumatic lightweight robots with articulated joints. Second, we demonstrate that the complex structural behavior of the actuator’s elastomeric bellows structure can be predicted by Finite Element (FE) simulation. To this end, a suitable hyperviscoelastic material model was calibrated and compared to recently published models in a multiaxial-state-of-stress relaxation experiment. To verify the material model, Finite Element simulations of the actuator’s deformation behavior were conducted, and the results compared to those of corresponding experiments. The simulations presented here advance the materials science of inkjet-printed elastomers by demonstrating use of a hyperviscoelastic material model for estimating the deformation behavior of a prototypic robotic component. The results obtained contribute to the long-term goal of additively manufactured and pneumatically actuated lightweight robots.
Leihao Chen, Michele Ghilardi, James J. C. Busfield,
Frontiers in Robotics and AI, Volume 8; doi:10.3389/frobt.2021.678046

Abstract:
Optical lenses with electrically controllable focal length are of growing interest, in order to reduce the complexity, size, weight, response time and power consumption of conventional focusing/zooming systems, based on glass lenses displaced by motors. They might become especially relevant for diverse robotic and machine vision-based devices, including cameras not only for portable consumer electronics (e.g. smart phones) and advanced optical instrumentation (e.g. microscopes, endoscopes, etc.), but also for emerging applications like small/micro-payload drones and wearable virtual/augmented-reality systems. This paper reviews the most widely studied strategies to obtain such varifocal “smart lenses”, which can electrically be tuned, either directly or via electro-mechanical or electro-thermal coupling. Only technologies that ensure controllable focusing of multi-chromatic light, with spatial continuity (i.e. continuous tunability) in wavefronts and focal lengths, as required for visible-range imaging, are considered. Both encapsulated fluid-based lenses and fully elastomeric lenses are reviewed, ranging from proof-of-concept prototypes to commercially available products. They are classified according to the focus-changing principles of operation, and they are described and compared in terms of advantages and drawbacks. This systematic overview should help to stimulate further developments in the field.
Kubra Akbas,
Frontiers in Robotics and AI, Volume 8; doi:10.3389/frobt.2021.648485

Abstract:
Mobility has been one of the most impacted aspects of human life due to the spread of the COVID-19 pandemic. Home confinement, the lack of access to physical rehabilitation, and prolonged immobilization of COVID-19-positive patients within hospitals are three major factors that affected the mobility of the general population world-wide. Balance is one key indicator to monitor the possible movement disorders that may arise both during the COVID-19 pandemic and in the coming future post-COVID-19. A systematic quantification of the balance performance in the general population is essential for preventing the appearance and progression of certain diseases (e.g., cardiovascular, neurodegenerative, and musculoskeletal), as well as for assessing the therapeutic outcomes of prescribed physical exercises for elderly and pathological patients. Current research on clinical exercises and associated outcome measures of balance is still far from reaching a consensus on a “golden standard” practice. Moreover, patients are often reluctant or unable to follow prescribed exercises, because of overcrowded facilities, lack of reliable and safe transportation, or stay-at-home orders due to the current pandemic. A novel balance assessment methodology, in combination with a home-care technology, can overcome these limitations. This paper presents a computational framework for the in-home quantitative assessment of balance control skills. Novel outcome measures of balance performance are implemented in the design of rehabilitation exercises with customized and quantifiable training goals. Using this framework in conjunction with a portable technology, physicians can treat and diagnose patients remotely, with reduced time and costs and a highly customized approach. The methodology proposed in this research can support the development of innovative technologies for smart and connected home-care solutions for physical therapy rehabilitation.
Aziza Alzadjali, Mohammed H. Alali, Arun Narenthiran Veeranampalayam Sivakumar, Jitender S. Deogun, Stephen Scott, James C. Schnable,
Frontiers in Robotics and AI, Volume 8; doi:10.3389/frobt.2021.600410

Abstract:
The timing of flowering plays a critical role in determining the productivity of agricultural crops. If the crops flower too early, the crop would mature before the end of the growing season, losing the opportunity to capture and use large amounts of light energy. If the crops flower too late, the crop may be killed by the change of seasons before it is ready to harvest. Maize flowering is one of the most important periods where even small amounts of stress can significantly alter yield. In this work, we developed and compared two methods for automatic tassel detection based on the imagery collected from an unmanned aerial vehicle, using deep learning models. The first approach was a customized framework for tassel detection based on convolutional neural network (TD-CNN). The other method was a state-of-the-art object detection technique of the faster region-based CNN (Faster R-CNN), serving as baseline detection accuracy. The evaluation criteria for tassel detection were customized to correctly reflect the needs of tassel detection in an agricultural setting. Although detecting thin tassels in the aerial imagery is challenging, our results showed promising accuracy: the TD-CNN had an F1 score of 95.9% and the Faster R-CNN had 97.9% F1 score. More CNN-based model structures can be investigated in the future for improved accuracy, speed, and generalizability on aerial-based tassel detection.
, Jocelyn Shen, Hae Won Park, Cynthia Breazeal
Frontiers in Robotics and AI, Volume 8; doi:10.3389/frobt.2021.683066

Abstract:
Across a wide variety of domains, artificial agents that can adapt and personalize to users have potential to improve and transform how social services are provided. Because of the need for personalized interaction data to drive this process, long-term (or longitudinal) interactions between users and agents, which unfold over a series of distinct interaction sessions, have attracted substantial research interest. In recognition of the expanded scope and structure of a long-term interaction, researchers are also adjusting the personalization models and algorithms used, orienting toward “continual learning” methods, which do not assume a stationary modeling target and explicitly account for the temporal context of training data. In parallel, researchers have also studied the effect of “multitask personalization,” an approach in which an agent interacts with users over multiple different tasks contexts throughout the course of a long-term interaction and learns personalized models of a user that are transferrable across these tasks. In this paper, we unite these two paradigms under the framework of “Lifelong Personalization,” analyzing the effect of multitask personalization applied to dynamic, non-stationary targets. We extend the multi-task personalization approach to the more complex and realistic scenario of modeling dynamic learners over time, focusing in particular on interactive scenarios in which the modeling agent plays an active role in teaching the student whose knowledge the agent is simultaneously attempting to model. Inspired by the way in which agents use active learning to select new training data based on domain context, we augment a Gaussian Process-based multitask personalization model with a mechanism to actively and continually manage its own training data, allowing a modeling agent to remove or reduce the weight of observed data from its training set, based on interactive context cues. We evaluate this method in a series of simulation experiments comparing different approaches to continual and multitask learning on simulated student data. We expect this method to substantially improve learning in Gaussian Process models in dynamic domains, establishing Gaussian Processes as another flexible modeling tool for Long-term Human-Robot Interaction (HRI) Studies.
, Emy Arts, Brenda Vasiljevic, Ankit Srivastava, Florian Schmalzl, Glareh Mir, Kavish Bhatia, Erik Strahl, Annika Peters, Tayfun Alpay, et al.
Frontiers in Robotics and AI, Volume 8; doi:10.3389/frobt.2021.644529

Abstract:
As robots become more advanced and capable, developing trust is an important factor of human-robot interaction and cooperation. However, as multiple environmental and social factors can influence trust, it is important to develop more elaborate scenarios and methods to measure human-robot trust. A widely used measurement of trust in social science is the investment game. In this study, we propose a scaled-up, immersive, science fiction Human-Robot Interaction (HRI) scenario for intrinsic motivation on human-robot collaboration, built upon the investment game and aimed at adapting the investment game for human-robot trust. For this purpose, we utilize two Neuro-Inspired COmpanion (NICO) - robots and a projected scenery. We investigate the applicability of our space mission experiment design to measure trust and the impact of non-verbal communication. We observe a correlation of 0.43 (p=0.02) between self-assessed trust and trust measured from the game, and a positive impact of non-verbal communication on trust (p=0.0008) and robot perception for anthropomorphism (p=0.007) and animacy (p=0.00002). We conclude that our scenario is an appropriate method to measure trust in human-robot interaction and also to study how non-verbal communication influences a human’s trust in robots.
Sergio D. Sierra Marín, Daniel Gomez-Vargas, Nathalia Céspedes, Marcela Múnera, Flavio Roberti, Patricio Barria, Subramanian Ramamoorthy, Marcelo Becker, Ricardo Carelli,
Frontiers in Robotics and AI, Volume 8; doi:10.3389/frobt.2021.612746

Abstract:
Several challenges to guarantee medical care have been exposed during the current COVID-19 pandemic. Although the literature has shown some robotics applications to overcome the potential hazards and risks in hospital environments, the implementation of those developments is limited, and few studies measure the perception and the acceptance of clinicians. This work presents the design and implementation of several perception questionnaires to assess healthcare provider's level of acceptance and education toward robotics for COVID-19 control in clinic scenarios. Specifically, 41 healthcare professionals satisfactorily accomplished the surveys, exhibiting a low level of knowledge about robotics applications in this scenario. Likewise, the surveys revealed that the fear of being replaced by robots remains in the medical community. In the Colombian context, 82.9% of participants indicated a positive perception concerning the development and implementation of robotics in clinic environments. Finally, in general terms, the participants exhibited a positive attitude toward using robots and recommended them to be used in the current panorama.
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