Domestic Robots Research Articles

Strategies for implementing pet robots in care homes and nursing homes for residents with dementia: protocol for a modified Delphi study

BackgroundPet robots are a type of technology-based innovation that have shown positive psychosocial benefits for people with dementia in residential facilities, such as improving mood and social interaction and reducing agitation. Nevertheless, little is known about how pet robots can be implemented in care homes and nursing homes for dementia care in real-world practice. The objectives of this study are to (1) identify contextualised implementation strategies for implementing pet robots into care homes and nursing homes for dementia care and (2) achieve consensus on the most relevant strategies.MethodThis study is informed by a preceding scoping review and qualitative study, which used the Consolidated Framework of Implementation Research (CFIR) to identify multi-level determinants of implementation (i.e. barriers and facilitators). We will use the CFIR-ERIC matching tool to identify relevant implementation strategies from the Expert Recommendations for Implementing Change (ERIC) taxonomy to address these determinants. Data from the scoping review and qualitative study will be used to contextualise the generic ERIC strategies for our setting. After that, a group of key stakeholders will be consulted to further contextualise and refine these strategies. Next, a two-round modified Delphi process will be conducted. Fifty-four international expert participants including healthcare professionals and organisational leaders from care homes and nursing homes and academic researchers will be recruited through purposive sampling. During the first Delphi round, participants will be invited to rate the relevance of each implementation strategy on a 9-point Likert scale and provide comments or suggestions. Descriptive statistics will be used to identify whether consensus has been obtained. Inductive qualitative content analysis will be used to analyse and summarise textual responses for any new statements suggested by participants. Statements that do not reach consensus and new statements suggested in round 1 will be taken to the next round, which will follow the same rating process.DiscussionThis study will identify strategies for implementing pet robots in care homes and nursing homes for residents with dementia, which will have practical utility for clinicians, organisations and researchers. It will also demonstrate the practical application (and adaptation) of the CFIR-ERIC tool to identify and contextualise ERIC strategies.

Security Based Home Automation Using IoT Devices

The Internet of things, or IOT, is an arrangement of interrelated figuring gadgets, mechanical and advanced machines, items, creatures or individuals that are given remarkable identifiers and the capacity to move information over an organization without expecting human-to-human or human-to-PC association. One of these applications is home robotization which is utilized to control all electrical home machines naturally of by means of advanced mobile phones. This controls gadgets through a Boolean orders of valid and bogus to control to gadgets. These dampness levels for wet and dry conditions (cases) can be changed relying upon the plant necessities for plant development. This can be utilized to identify soil dampness when the dirt dampness shortage module plant watering gadget, so the plants in our nursery without individuals to make due. This aides in numerous ways like while are getting back to home and you need the AC to be on and be cooled before you happen to assuming that you neglect to turn of light or fan you want not again come to home and turn off them, rather than it you can handle with your advanced mobile phone when any place you are. This undertaking incorporates IFTTT, Blynk and Node MCU(ESP32) . At first the gadgets are associated with the Node MCU(ESP32) to the regarded ports then the sign from the Blynk application will give signs to the Node MCU(ESP32) then, at that point, the regarded port will give power supply to the gadget through Relay board and the gadget will turn on. The Blynk application is associated with google aide, through IFTTT. In IFTTT the google right hand and web hook is associated together to make this project achievement. Assuming the form in laud is given as a contribution to research aide then the gadget will turn on.

Show Me What You Can Do: Capability Calibration on Reachable Workspace for Human-Robot Collaboration

Aligning humans’ assessment of what a robot can do with its true capability is crucial for establishing a common ground between human and robot partners when they collaborate on a joint task. In this work, we propose an approach to calibrate humans’ estimate of a robot’s reachable workspace through a small number of demonstrations <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">before</i> collaboration. We develop a novel motion planning method, reachability-expressive motion planning (REMP), which jointly optimizes the physical cost and the expressiveness of robot motion to reveal the robot’s reachability to a human observer. Our experiments with human participants demonstrate that a short calibration using REMP can effectively align a non-expert user’s estimation of the robot’s reachability with its true capacity. We show that this calibration procedure not only results in better user perception, but also promotes more efficient human-robot collaborations in a subsequent joint task. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>

Design patterns for human-AI co-learning: A wizard-of-Oz evaluation in an urban-search-and-rescue task

The rapid advancement of technology empowered by artificial intelligence is believed to intensify the collaboration between humans and AI as team partners. Successful collaboration requires partners to learn about each other and about the task. This human-AI co-learning can be achieved by presenting situations that enable partners to share knowledge and experiences. In this paper we describe the development and implementation of a task context and procedures for studying co-learning. More specifically, we designed specific sequences of interactions that aim to initiate and facilitate the co-learning process. The effects of these interventions on learning were evaluated in an experiment, using a simplified virtual urban-search-and-rescue task for a human-robot team. The human participants performed a victim rescue- and evacuation mission in collaboration with a wizard-of-Oz (i.e., a confederate of the experimenter who executed the robot-behavior consistent with an ontology-based AI-model). The designed interaction sequences, formulated as Learning Design Patterns (LDPs), were intended to bring about co-learning. Results show that LDPs support the humans understanding and awareness of their robot partner and of the teamwork. No effects were found on collaboration fluency, nor on team performance. Results are used to discuss the importance of co-learning, the challenges of designing human-AI team tasks for research into this phenomenon, and the conditions under which co-learning is likely to be successful. The study contributes to our understanding of how humans learn with and from AI-partners, and our propositions for designing intentional learning (LDPs) provide directions for applications in future human-AI teams.

Utilization of Image/Force/Tactile Sensor Data for Object-Shape-Oriented Manipulation: Wiping Objects With Turning Back Motions and Occlusion

There has been an increasing demand for housework robots to handle various objects. It is, however, difficult to achieve object-shape-oriented tasks in conventional research owing to the requirement for dealing with multiple surfaces, invisible area, and occlusion; moreover, robots must perceive shapes and adjust movements even if they cannot be seen directly. Humans usually tackle questions by integrating several sensory information; inspired by this perception mechanism of humans, in this study, we considered the effective utilization of image/force/tactile data in constructing a multimodal deep neural networks (DNN) model for the shape of an object perception and motion generation. As an example, we constructed a robot to wipe around the outside of objects that are imitating light shades. The wiping motions include the moment when the hands of the robot must be away from the surface as well as the turning directions required to wipe the next surface, even though some parts of the surfaces, such as the backside or parts occluded by the arm of the robot, may not be seen directly. If DNN model uses continuous visual information, it is badly influenced by the occluded images. Hence, the best-performing DNN model is the one that uses an image of the initial time-step to approximately perceive the shape and size and then generate motions by integrating the perception and sense of tactile and force. We conclude that the effective approach to object-shape-oriented manipulation is to initially utilize image to outline the target shape and, thereafter, to use force and tactile to understand concrete features while performing tasks.

Passive Bimanual Skills Learning From Demonstration With Motion Graph Attention Networks

Enabling household robots to passively learn task-level skills from human demonstration could substantially boost their application in daily life. In this letter, we propose a Learning from Demonstration (LfD) scheme capturing human uni/bimanual demonstrations with motion capture suit and virtual reality (VR) trackers, wherein the demonstrated skills are transferred to a humanoid with a learnable graph attention network (GAT) based model. The model trained with human hand trajectories and target object poses yield the movement policy as a trajectory generator, which outputs the Cartesian trajectories for robot end-effectors to execute the task given their poses and optionally the object’s initial pose as input. Test on synthetic data and three real robot experiments indicated that the policy could learn unimanual and coordinated bimanual, interactive and non-interactive manipulation skills with a unified scheme.

Discrete HMM for Visualizing Domiciliary Human Activity Perception and Comprehension

Advances in artificial intelligence-based autonomous applications have led to the advent of domestic robots for smart elderly care; the preliminary critical step for such robots involves increasing the comprehension of robotic visualizing of human activity recognition. In this paper, discrete hidden Markov models (D-HMMs) are used to investigate human activity recognition. Eleven daily home activities are recorded using a video camera with an RGB-D sensor to collect a dataset composed of 25 skeleton joints in a frame, wherein only 10 skeleton joints are utilized to efficiently perform human activity recognition. Features of the chosen ten skeleton joints are sequentially extracted in terms of pose sequences for a specific human activity, and then, processed through coordination transformation and vectorization into a codebook prior to the D-HMM for estimating the maximal posterior probability to predict the target. In the experiments, the confusion matrix is evaluated based on eleven human activities; furthermore, the extension criterion of the confusion matrix is also examined to verify the robustness of the proposed work. The novelty indicated D-HMM theory is not only promising in terms of speech signal processing but also is applicable to visual signal processing and applications.

Mind Your Manners! A Dataset and a Continual Learning Approach for Assessing Social Appropriateness of Robot Actions.

To date, endowing robots with an ability to assess social appropriateness of their actions has not been possible. This has been mainly due to (i) the lack of relevant and labelled data and (ii) the lack of formulations of this as a lifelong learning problem. In this paper, we address these two issues. We first introduce the Socially Appropriate Domestic Robot Actions dataset (MANNERS-DB), which contains appropriateness labels of robot actions annotated by humans. Secondly, we train and evaluate a baseline Multi Layer Perceptron and a Bayesian Neural Network (BNN) that estimate social appropriateness of actions in MANNERS-DB. Finally, we formulate learning social appropriateness of actions as a continual learning problem using the uncertainty of Bayesian Neural Network parameters. The experimental results show that the social appropriateness of robot actions can be predicted with a satisfactory level of precision. To facilitate reproducibility and further progress in this area, MANNERS-DB, the trained models and the relevant code are made publicly available at https://github.com/jonastjoms/MANNERS-DB.

The Dawn of the AI Robots: Towards a New Framework of AI Robot Accountability

Business, management, and business ethics literature pay little attention to the topic of AI robots. The broad spectrum of potential ethical issues pertains to using driverless cars, AI robots in care homes, and in the military, such as Lethal Autonomous Weapon Systems. However, there is a scarcity of in-depth theoretical, methodological, or empirical studies that address these ethical issues, for instance, the impact of morality and where accountability resides in AI robots’ use. To address this dearth, this study offers a conceptual framework that interpretively develops the ethical implications of AI robot applications, drawing on descriptive and normative ethical theory. The new framework elaborates on how the locus of morality (human to AI agency) and moral intensity combine within context-specific AI robot applications, and how this might influence accountability thinking. Our theorization indicates that in situations of escalating AI agency and situational moral intensity, accountability is widely dispersed between actors and institutions. ‘Accountability clusters’ are outlined to illustrate interrelationships between the locus of morality, moral intensity, and accountability and how these invoke different categorical responses: (i) illegal, (ii) immoral, (iii) permissible, and (iv) supererogatory pertaining to using AI robots. These enable discussion of the ethical implications of using AI robots, and associated accountability challenges for a constellation of actors—from designer, individual/organizational users to the normative and regulative approaches of industrial/governmental bodies and intergovernmental regimes.

Pragmatic inferences in aging and human-robot communication

Despite the increase in research on older adults' communicative behavior, little work has explored patterns of age-related change in pragmatic inferencing and how these patterns are adapted depending on the situation-specific context. In two eye-tracking experiments, participants followed instructions like “Click on the greenhouse”, which were either played over speakers or spoken live by a co-present robot partner. Implicit inferential processes were measured by exploring the extent to which listeners temporarily (mis)understood the unfolding noun to be a modified phrase referring to a competitor object in the display (green hat). This competitor was accompanied by either another member of the same category or an unrelated item (tan hat vs. dice). Experiment 1 (no robot) showed clear evidence of contrastive inferencing in both younger and older adults (more looks to the green hat when the tan hat was also present). Experiment 2 explored the ability to suppress these contrastive inferences when the robot talker was known to lack any color perception, making descriptions like “green hat” implausible. Younger but not older listeners were able to suppress contrastive inferences in this context, suggesting older adults could not keep the relevant limitations in mind and/or were more likely to spontaneously ascribe human attributes to the robot. Together, the findings enhance our understanding of pragmatic inferencing in aging.

Tele-robotic recommendation framework using multi-dimensional medical datasets on COVID-19 classification

The development of robotic partners to take care of daily human life has been expanded recently. Mobile robots have spread their presence within the public environment to assist people in a variety of problematic activities. Mobile Robots are developed with the underlying artificial intelligence technology. Adequate training is provided to the mobile robots under the classifications of supervised learning. The interaction of robots is very important to practice everything that is told to the robotic systems from domestic robots to high-risk work environments that threaten the health of the spinal cord, which focuses on robotic support during the COVID-19 epidemic. In the present research work, a mobile agent is trained using Computerized Tomography (CT) scan reports and X-rays under VGG-16 processing standards for classifying covid and non-covid patients. A hybrid model is designed using Deep Learning Network (DNN) and Convolutional Neural Network (CNN). CNN is trained using images collected using a camera and thermal camera with RGB values ranging from 0 to 255. The advantage of the proposed model in training the mobile agent is making use of CT scan and X-ray images and providing recommendations to the victim about the criticality of being affected by covid. In addition to that, the Machine Learning Algorithm like Decision Tree and Random Forest is constructed and achieved a classification accuracy of 95%. The proposed technique has efficiently provided a reliable recommendation system based on ReLu activation. The other evaluation parameters used to estimate the performance of the proposed model are precision, recall, F1-score. The proposed model achieves 0.84 Precision over the inception technique with 0.79 precision. The reason behind the improvement of accuracy in the present work is the filter used to extract the features.

Optical laser microphone for human-robot interaction: speech recognition in extremely noisy service environments

Domestic robots are often required to understand spoken commands in noisy environments, including service appliances' operating sounds. Most conventional domestic robots use electret condenser microphones (ECMs) to record the sound. However, the ECMs are known to be sensitive to the noise in the direction of sound arrival. The laser Doppler vibrometer (LDV), which has been widely used in the research field of measurement, has the potential to work as a new speech-input device to solve this problem. The aim of this paper is to investigate the effectiveness of using the LDV as an optical laser microphone for human-robot interaction in extremely noisy service environments. Our robot irradiates an object near a speaker with a laser and measures the vibration of the object to record the sound. We conducted three experiments to assess the performance of speech recognition using the optical laser microphone in various settings and showed stable performance in extremely noisy conditions compared with a conventional ECM.

Target Positioning and Grasping of NAO Robot Based on Monocular Stereo Vision

With the continuous advancement of science and technology and the rapid development of robotics, it has become an inevitable trend for domestic robots to enter thousands of households. In order to solve the inconvenience problem of the elderly and people with special needs, because the elderly and other people in need may need the help of domestic robots due to inconvenient legs and feet, the research of the robot target position based on monocular stereo vision and the understanding of the robot NAO are very important. Research and experiments are carried out on the target recognition and positioning in the process of NAO robot grasping. This paper proposes a recognition algorithm corresponding to quantitative component statistical information. First, extract the area of interest that contains the purpose from the image. After that, to eliminate interference in various fields and achieve target recognition, the robot cameras have almost no common field of view and can only use one camera at the same time. Therefore, this article uses the monocular vision principle to locate the target, and the detection algorithm is based on the structure of the robot head material, establishes the relationship between the height change of the machine head and the tilt angle, and improves the monocular vision NAO robot detection algorithm. According to experiments, the accuracy of the robot at close range can be controlled below 1 cm. This article completes the robot’s grasping and transmission of the target. About 80% of the external information that humans can perceive comes from vision. In addition, there are advantages such as high efficiency and good stability.

More work for Roomba? Domestic robots, housework and the production of privacy

Housework is hard work. Keeping our homes clean, tidy and comfortable takes effort and every moment we spend on housework (that we would prefer to avoid) means we have less time to devote to our private lives. Over the past two decades, numerous companies have created robots designed to relieve their owners of housework. Having robots take care of housework for us, it seems, would enable us to focus our energy at home on private pursuits we find valuable, such as spending quality time with our loved ones, recreation, and relaxation. Although this line of reasoning helps explain why domestic robots are in high demand, this article will contest its validity throughout. By drawing from historical accounts of older, ostensibly labour-saving domestic technologies, it will argue that we should expect domestic robots to alter the nature of housework rather than reduce the need for it. Overall, it will argue that domestic robots change what needs to be done for their owners to enjoy their private lives.

Topological mapping based on perceiving-acting cycle in sharing cognitive environments for robot partners

Topological mapping based on perceiving-acting cycle in sharing cognitive environments for robot partners

ОСОБЛИВОСТІ ІНТЕЛЕКТУАЛІЗАЦІЇ ВИРОБНИЧИХ ТЕХНОЛОГІЙ НА ПІДПРИЄМСТВАХ ЗАЛІЗНИЧНОГО ТРАНСПОРТУ

The article proves the need to intensify the activities of railway transport enterprises, which is due to the transition of these business entities to digital, intelligent and robotic systems, in turn, this has both economic and social prerequisites. To increase the robotization of enterprises in the industry, it is necessary to use the appropriate organizational and economic mechanism, which should be built taking into account the peculiarities of the functioning of railway transport enterprises. This mechanism should contain three components (target, investment, technical and technological), each of which should perform certain functions. The mechanism should be considered as a certain set of elements having systemic properties, the most important of which is target orientation. It will make it possible to minimize the above-mentioned problems, to use effective tools for the transition of the agricultural sector to digital and robotic systems, to ensure sustainable rates of development and modernization of agricultural production. The target component consists in determining the target orientation of the introduction of robotics at railway transport enterprises. The investment component should include market research of railway robotics, subsidizing the cost of purchasing robotics, regulating the pricing of robotics, and determining the amount of investment in the robotics of industry enterprises. The technical and technological component includes the identification of the needs of railway transport enterprises in robotics, the selection of robotics that best meets the requirements of a specific production unit in terms of capacity, the development of recommendations for the use of robotics for them, research and development works for the creation of robotics and digital technologies. Within the target component, three possible scenarios for the development of domestic railway robotics were identified, the implementation of which is inextricably linked to state support for this activity: inertial scenario: preservation of the state's existing positions in this field, in which railway robotics is not among the priorities of state support; scenario of fragmented support: concentration of efforts on the creation of domestic robotics in the specified individual directions; system support scenario: qualitative positive changes in the creation and mass implementation of robotics at railway transport enterprises.

Progressive Glass Segmentation.

Glass is very common in the real world. Influenced by the uncertainty about the glass region and the varying complex scenes behind the glass, the existence of glass poses severe challenges to many computer vision tasks, making glass segmentation as an important computer vision task. Glass does not have its own visual appearances but only transmit/reflect the appearances of its surroundings, making it fundamentally different from other common objects. To address such a challenging task, existing methods typically explore and combine useful cues from different levels of features in the deep network. As there exists a characteristic gap between level-different features, i.e., deep layer features embed more high-level semantics and are better at locating the target objects while shallow layer features have larger spatial sizes and keep richer and more detailed low-level information, fusing these features naively thus would lead to a sub-optimal solution. In this paper, we approach the effective features fusion towards accurate glass segmentation in two steps. First, we attempt to bridge the characteristic gap between different levels of features by developing a Discriminability Enhancement (DE) module which enables level-specific features to be a more discriminative representation, alleviating the features incompatibility for fusion. Second, we design a Focus-and-Exploration Based Fusion (FEBF) module to richly excavate useful information in the fusion process by highlighting the common and exploring the difference between level-different features. Combining these two steps, we construct a Progressive Glass Segmentation Network (PGSNet) which uses multiple DE and FEBF modules to progressively aggregate features from high-level to low-level, implementing a coarse-to-fine glass segmentation. In addition, we build the first home-scene-oriented glass segmentation dataset for advancing household robot applications and in-depth research on this topic. Extensive experiments demonstrate that our method outperforms 26 cutting-edge models on three challenging datasets under four standard metrics. The code and dataset will be made publicly available.

Exploring Social and Assistive Domestic Robots for Older Adults: Robot Sociability, Trust, and Acceptance

Older adults prefer to age in place, to live independently while maintaining social connection and engagement with the community. Though older adults can encounter barriers to these goals, social and assistive domestic robots hold promise for promoting independence and online/offline social engagement. However, social robots must be designed to meet their needs and preferences. Open questions remain regarding how to facilitate the development of trust and acceptance in robot support. We investigated the associations between robot social characteristics, sociability, trust, and acceptance for instrumental activities of daily living. We used an online survey to assess older adults’ perceptions towards social and assistive robots. Robots with more social abilities were rated as more acceptable and trustworthy across different task types. We discuss design implications that may promote the development of robot trust and acceptance by older adults, and ultimately help enable aging in place and social engagement.

A Biological Inspired Cognitive Framework for Memory-Based Multi-Sensory Joint Attention in Human-Robot Interactive Tasks.

One of the fundamental prerequisites for effective collaborations between interactive partners is the mutual sharing of the attentional focus on the same perceptual events. This is referred to as joint attention. In psychological, cognitive, and social sciences, its defining elements have been widely pinpointed. Also the field of human-robot interaction has extensively exploited joint attention which has been identified as a fundamental prerequisite for proficient human-robot collaborations. However, joint attention between robots and human partners is often encoded in prefixed robot behaviours that do not fully address the dynamics of interactive scenarios. We provide autonomous attentional behaviour for robotics based on a multi-sensory perception that robustly relocates the focus of attention on the same targets the human partner attends. Further, we investigated how such joint attention between a human and a robot partner improved with a new biologically-inspired memory-based attention component. We assessed the model with the humanoid robot iCub involved in performing a joint task with a human partner in a real-world unstructured scenario. The model showed a robust performance on capturing the stimulation, making a localisation decision in the right time frame, and then executing the right action. We then compared the attention performance of the robot against the human performance when stimulated from the same source across different modalities (audio-visual and audio only). The comparison showed that the model is behaving with temporal dynamics compatible with those of humans. This provides an effective solution for memory-based joint attention in real-world unstructured environments. Further, we analyzed the localisation performances (reaction time and accuracy), the results showed that the robot performed better in an audio-visual condition than an audio only condition. The performance of the robot in the audio-visual condition was relatively comparable with the behaviour of the human participants whereas it was less efficient in audio-only localisation. After a detailed analysis of the internal components of the architecture, we conclude that the differences in performance are due to egonoise which significantly affects the audio-only localisation performance.

Home Robot Control System Based on Internet of Things and Fuzzy Control

The rapid development of intelligent control technology has improved the functions of service robots oriented to the home environment, and the functional requirements of family members for service robots have also been upgraded from simply liberating hands and reducing housework to emotional communication and intelligent escort. Based on the Internet of Things and fuzzy control technology, this paper builds a home robot control system and gives a brief overview around the mechanical structure design of the home service robot, mainly focusing on the core control system and global path planning methods. Moreover, this paper adopts the control system structure that combines the upper computer and the bottom motion controller and combines it with simple and practical system software, so the system stability is high. Finally, this paper verifies the performance of the system constructed in this paper through experimental research. The research results show that the system constructed in this paper has certain practical effects.