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In order to improve agricultural efficiency, reduce labor intensity, and improve the quality and yield of agricultural products on the Tibetan Plateau, this paper takes the application of traditional agricultural robots as an example and proposes the application of agricultural robots to agriculture on the Qinghai-Tibet Plateau based on the special breeding environment of the Qinghai-Tibet Plateau combined with the current situation of traditional agriculture. Through the application practice of robots in traditional agriculture, the possibility of the application of agricultural robots in the Qinghai-Tibet Plateau is pointed out, the working methods and advantages of robots in the Qinghai-Tibet Plateau are expounded, and the application of intelligent agricultural robots in the agriculture of the Qinghai-Tibet Plateau is discussed and analyzed. The results show that with the progress of science and technology, intelligent robots have gradually played an important role in various fields, and intelligent robots have broad application prospects in plateau agriculture. The application of agricultural robots in the Qinghai-Tibet Plateau should be aimed at the Qinghai-Tibet Plateau to make some adaptive research and development, intelligent upgrading and demonstration project promotion in order to promote the Qinghai-Tibet Plateau agricultural automation. [ABSTRACT FROM AUTHOR]

Online public opinion incidents in universities occur from time to time, with the source difficult to find, rapid dissemination, and escalating fragmentation. There are many problems and difficulties in the guidance of online public opinion in universities, and a new way out needs to be explored. This article explores the influencing factors of intelligent robots on online education in universities in the all-media era. This article believes that the ecology of university online education is an inherent component of university online culture and is the result of the interaction and mutual restraint between various elements and external environmental factors. The self-regulation of each element gradually tends towards a harmonious and stable state. It is closely related to cultural ecology, network cultural ecology, and university network culture, and also has its own unique characteristics. Establish a collaborative governance model between university public opinion work alliances and society, improve the application standards of artificial intelligence and the management mechanism of IGM and MGC technologies. Compared with traditional technologies, intelligent robots in the all-media era can guide the network public opinion of colleges and universities in the intelligent Internet era. [ABSTRACT FROM AUTHOR]

User‐interactive interfaces, converting tactile stimuli into readable signals to users and devices simultaneously, improve the communication and interaction between human and machines and therefore greatly contribute to the safety and dexterity during the interactions. However, the concomitant challenges of current user‐interactive interfaces such as complex architecture, massive electrodes and fussy cable connections, bulky and unhandy power supply, and deficiency in multistimuli responses have yet to be solved. Herein, an all‐optical tactile sensing platform consisting of heterogeneous mechanoluminescent materials and polymer matrix is proposed for the conversion of multiple tactile stimuli into heterochromatic lights in an untethered and self‐powered manner. The all‐optical tactile sensing platform can respond to tiny shear force such as fingertip slipping with a low limit of 2 N and wide range of strains ranging from 30% to 70% with appropriate discrimination. Most importantly, the visualization of tactile stimuli with human‐ and machine‐readability and vividness permit remote and wireless user‐interactive applications such as videogames and RC car control, assisted with the developed method of active optical signal recognition. This work presents a paradigm shift to user‐interactive interfaces, boosting their implementation in multitudinous areas such as artificial intelligences and the Internet of Things. [ABSTRACT FROM AUTHOR]

Bipedal robots have gained increasing attention for their human-like mobility which allows them to work in various human-scale environments. However, their inherent instability makes it difficult to control their balance while they are physically interacting with the environment. This study proposes a novel balance controller for bipedal robots based on a behavior cloning model as one of the machine learning techniques. The behavior cloning model employs two deep neural networks (DNNs) trained on human-operated balancing data, so that the trained model can predict the desired wrench required to maintain the balance of the bipedal robot. Based on the prediction of the desired wrench, the joint torques for both legs are calculated using robot dynamics. The performance of the developed balance controller was validated with a bipedal lower-body robotic system through simulation and experimental tests by providing random perturbations in the frontal plane. The developed balance controller demonstrated superior performance with respect to resistance to balance loss compared to the conventional balance control method, while generating a smoother balancing movement for the robot. [ABSTRACT FROM AUTHOR]

The foot paddling of an underwater robot causes continuous changes of the water flow field, which results in the unbalanced hydrodynamic force to change the robot's posture continuously. As the water environment and robot swimming are nonlinear and strongly coupled systems, it is difficult to establish an accurate model. This paper presents an underwater robot, which adopts the synchronous and alternate swimming trajectory of a beaver. Its pitch stability control model is established by using deep reinforcement learning algorithm and its self-learning control system is constructed for stable control of pitch attitude. Experiments are conducted to show that the pitch attitude of the beaver-like underwater robot can be stabilized while maintaining a certain swimming speed. The control method does not need to establish a complex and high-order model of webbed paddling hydrodynamics, which provides a new idea for stable swimming control of underwater robots. This work aims to find an excellent control method for underwater bionic robots. The ocean has the richest natural resources and the most diverse species on Earth. The underwater environment is complex and variable, imposing higher demands on the performance of underwater robots. Increasingly, new concept marine equipment is being researched for scientific exploration, and among these, underwater robots designed based on bionic principles are a growing trend. Currently, most underwater robots still use propellers as their propulsion system. Propellers have advantages such as simple control, high mechanical efficiency, and powerful propulsion, but they also have drawbacks including severe water flow disturbance during operation, high noise, poor concealment, and limited adaptability in complex water environments. Finding a propulsion system with better overall performance is a crucial way to enhance the motion capabilities of underwater robots. Underwater robots often have complex structures, and there are numerous factors influencing their movement in the underwater environment, making fluid dynamics modeling and optimization challenging. Reinforcement learning, as an optimization algorithm, can circumvent the aforementioned difficulties. • An underwater robot adopting the synchronous and alternate swimming trajectory of a beaver is presented. • The pitch stability control model is established using DRL and the self-learning control system is constructed for stable control of pitch attitude. • Experiments are conducted to show the pitch attitude of the beaver-like robot can be stabilized while maintaining a certain swimming speed. [ABSTRACT FROM AUTHOR]

Background: The twenty first- century marked the exponential growth in the use of intelligent robots and artificial intelligent in nursing compared to the previous decades. To the best of our knowledge, this article is first in responding to question, "Can nurses in clinical practice ascribe responsibility to intelligent robots and artificial intelligence when they commit errors?". Purpose: The objective of this article is to present two worldviews (anthropocentrism and biocentrism) in responding to the question at hand chosen based on the roles of the entities involved in the use of intelligent robots and artificial intelligence in nursing. Methods: The development of this article was motivated by the immense discoveries, the current landscape, and nurses' role in relation to advanced technologies in healthcare. The paper begins the discussion by situating the use of intelligent robots and artificial intelligence in nursing and healthcare and presenting its ethical and moral implications. Then, we presented the two worldviews: anthropocentrism and biocentrism which are used to respond to the task at hand. Results: Anthropocentrism puts humans in the center in terms of moral standing and thus responsibility rests on them alone. Biocentrism declares that all creations deserve moral consideration and thus responsibility is equally allocated to all entities. Within these two worldviews, consensus development was offered to resolve these issues. Consensus provides clarity and democracy between and among the societies. Conclusions: The findings of this article can be basis in (1) instituting mechanisms of robust peer review and a rigorous series of simulation before adopting or implementing intelligent robots and artificial intelligence in clinical practice; (2) education and training of highly specialized nurse practitioners who can be focal persons in responding to ethical and moral issues with regard to these advanced technologies; and (3) harmonization of robotics research, manufacturing, and clinical practice. [ABSTRACT FROM AUTHOR]

Currently, visual-tactile fusion learning for robotic object recognition has achieved appealing performance, due to the fact that visual and tactile data can offer complementary information. However: 1) the distinct gap between vision and touch makes it difficult to fully explore the complementary information, which would further lead to performance degradation and 2) most of the existing visual-tactile fused learning methods assume that visual and tactile data are complete, which is often difficult to be satisfied in many real-world applications. In this article, we propose a partial visual-tactile fused (PVTF) framework for robotic object recognition to address these challenges. Specifically, we first employ two modality-specific (MS) encoders to encode partial visual-tactile data into two incomplete subspaces (i.e., visual subspace and tactile subspace). Then, a modality gap mitigated (MGM) network is adopted to discover modality-invariant high-level label information, which is utilized to generate gap loss and further help updating the MS encoders for relatively consistent visual and tactile subspaces generation. In this way, the huge gap between vision and touch is mitigated, which would further contribute to mine the complementary visual-tactile information. Finally, to achieve data completeness and complementary visual-tactile information exploration simultaneously, a cycle subspace leaning technique is proposed to project the incomplete subspaces into a complete subspace by fully exploiting all the obtainable samples, where complete latent representations with maximum complementary information can be learned. A lot of comparative experiments conducted on three visual-tactile datasets validate the advantage of the proposed PVTF framework, by comparing with state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

Sampling-based planning methods often become inefficient due to narrow passages. Narrow passages induce a higher runtime, because the chance to sample them becomes vanishingly small. In recent work, we showed that narrow passages can be approached by relaxing the problem using admissible lower dimensional projections of the state space. Those relaxations often increase the volume of narrow passages under projection. Solving the relaxed problem is often efficient and produces an admissible heuristic we can exploit. However, given a base path, i.e., a solution to a relaxed problem, there are currently no tailored methods to efficiently exploit the base path. To efficiently exploit the base path and thereby its admissible heuristic, we develop section patterns, which are solution strategies to efficiently exploit base paths in particular around narrow passages. To coordinate section patterns, we develop the pattern dance algorithm, which efficiently coordinates section patterns to reactively traverse narrow passages. We combine the pattern dance algorithm with previously developed multilevel planning algorithms and benchmark them on challenging planning problems like the Bugtrap, the double L-shape, an egress problem, and on four pregrasp scenarios for a 37 degrees-of-freedom shadow hand mounted on a KUKA LWR robot. Our results confirm that section patterns are useful to efficiently solve high-dimensional narrow passage motion planning problems. [ABSTRACT FROM AUTHOR]

China has the world’s largest elderly population, as well as the largest population of disabled elderly. Existing literature shows that there have been increased efforts to develop intelligent robots to address the shortage of caregivers for the elderly. However, there has been little research on elders’ acceptance of care from intelligent robots.The purpose of this study was to examine the acceptance of intelligent robots among disabled elderly in need of long-term care.We used structured questionnaires and interview methods during a social survey. In total, 3060 questionnaires and 50 interviews were collected.An overwhelming majority of the 3060 participants (79.39%) indicated that they would reject robot care.Future research should consider the background of the interviewees and the influence of the economy and social development across different regions, in order to distinguish the impact of each type of basic information on the acceptance of intelligent machines by the elderly.Most elderly adults were reluctant to allow robots to provide services that involved physical contact. Therefore, the development and design of robots should consider elderly individuals’ attitudes toward non-human services, in addition to broadening the scope of activities that intelligent robots could cover, and providing intelligent tools and products that the elderly would be more likely to gradually accept. [ABSTRACT FROM AUTHOR]

Tennis is a set of sports and entertainment and a sports activity, since 2014, tennis in China has been another rapid development. With the development of economy and technology, tennis training mode has been further optimized and reformed. At present, tennis training robot is the mainstream way to train athletes. However, there are some defects in the current tennis training robots, such as the low accuracy of human motion real-time evaluation, and the lack of stability. Therefore, this paper puts forward the related research on the real-time evaluation algorithm of human motion in tennis training robots, hoping to make up for the deficiency in this field. The research of this paper is mainly divided into four parts. The first part is to analyze the current situation of technology research in this field and put forward the idea of this paper by analyzing the shortcomings of the existing technology. The second part is the related basic theory research; this part deeply studies the core theory of tennis training and intelligent training robot, which provides a theoretical basis for the realization of the optimization scheme. The third part is the design and implementation of a real-time human motion evaluation optimization algorithm for tennis training robots. At the end of the paper, that is, the fourth part, through the way of field test and investigation, further proves the superiority of the improved real-time evaluation algorithm of human movement. The algorithm has good stability and accuracy and can meet the existing tennis training requirements. [ABSTRACT FROM AUTHOR]

Social bots are computer software designed for content production and interaction with humans. With the popularity of images in social networks, social bots need to have visual awareness of image content while only understanding texts is far from enough to be active in social networks. We introduce a novel task, Visual Social Comment (VSC), in which social bots should generate relevant and informative comments on social contents of both images and texts. In this task of multimodal context, our work focuses on how to extract and fuse the information of vision and text to improve the quality of generated comments, and how to deal with the problem that neural dialog models trained with maximum likelihood estimation (MLE) criteria tend to generate generic responses. In order to fuse visual and textual context features closely through the relationship between them, we adopt joint attention of multimodal context to modify the standard sequence-to-sequence (Seq2Seq) framework. We also leverage the topic information transferred from a topic classification model to build a perceptual loss function, which encourages the generative comment model to generate more informative and diverse comments with the topic corresponding to context. The experimental results of models trained with data from Sina Weibo show that comments generated by our proposed models achieve better performance in both relevance and informativeness than those generated by other baseline models. [ABSTRACT FROM AUTHOR]

Snap-fit assemblies are widely used in the manufacturing of several product types, allowing part joining, while the parts remain unprocessed. The locking mechanism of a snap-fit is usually done within the object structure, not allowing visual identification of the successful process completion. Humans consider the forces developed between the two parts or the snapping sound, as an indication of success. This is difficult to realize in robotic assembly, and the process success is usually identified at a product quality control stage. The aim of this article is to migrate the human ability to identify a successful snap assembly to autonomous robotic assembly, via a machine learning framework, enabled by human–robot collaboration for rich data collection and labeling. The proposed framework allows learning while minimizing complexity, cost, and time. A generic feature set is proposed, which can produce good identification results in different snap assembly types. A feature transformation is also introduced that is fundamental for the real-time operation of the proposed framework and the identification of successful snap-assemblies. Three different objects are used to experimentally validate the approach using a KUKA LWR4+ robotic arm, resulting in high classification and real-time identification accuracy. Finally, a comparison with a model-based method is conducted. Note to Practitioners—This article is motivated by the need for flexible robotic assembly processes, and specifically the characterization of snap-fits, an assembly-type widely used in automated robotic processes. The snap-fit completion cannot be easily identified from a visual inspection, but the final result is usually realized by sensing the developed forces or identifying the snapping sound during a manual assembly. Training industrial robots to imitate the human snap-fit identification will allow accurate monitoring of such assemblies and the automation of the process with certainty for the final result. A quick and accurate perception of the process status can improve the overall process deployment speed and efficiency of such assemblies. The proposed framework allows the real-time characterization of the snap-fit process with very high accuracy and is easily implemented with collaborative robots. The framework can be further extended to other assembly processes using assembly forces as an indication signal. [ABSTRACT FROM AUTHOR]

The fusion of sensor data is an essential requirement for active perception in autonomous robots in order to accurately perceive an environment, make appropriate adjustments that improve the understanding of the environment and generate actionable insights. Despite a large body of literature in this domain, only a handful of research focuses on unsupervised machine learning, which is increasingly important in unlabeled, unknown environments. This paper proposes a scalable self-organizing neural architecture for environmental perception based on multimodal fusion using unsupervised machine learning. Inspired by the biological counterpart, the neural architecture consists of topographic maps across each modality and incorporates a novel scalable self-organization process that handles high volume, high-velocity sensor data. The co-occurrence relationships across modalities are captured in cross-modal connections and the reverse Hebbian projection is used for multimodal representation. This proposed unsupervised machine learning-based scalable fusion technique was implemented on Apache Hadoop and Apache Spark. It was validated using an extensive multimodal human activity sensor dataset to demonstrate efficient representation and multimodal fusion for active perception. Note to Practitioners—Heterogeneous sensors are increasingly used to capture different aspects of an event or an environment. This paper presents a multimodal fusion mechanism to fuse such heterogeneous sensor data to support the accurate perception of the surrounding. The proposed mechanism uses natural regularities in patterns observed across multiple modalities and enriches each modality with information from other co-occurring modalities to achieve a multimodal clustering. The cluster can be used for online prediction and offline action planning of the robot by matching the current sensory inputs. The algorithm uses unsupervised machine learning, and hence, can be used with any multimodal dataset without being restricted by the labeling of the dataset. In addition, we adapt the algorithm for multiple big data platforms so that it is effective and pragmatic for large-scale sensor data fusion tasks. [ABSTRACT FROM AUTHOR]

Abstract Relative to their urban peers, rural children have highly limited access to educational resource and lack of parental care, especially for left-behind children. This study used 156 rural children to evaluate an innovative approach, by the Ensan Charity Foundation to improve educational resources and care for rural children in China. The findings indicate that on average, the children used the foundation's Xiao En Intelligent Robots 1.3 h daily. The usage of and satisfaction with the robots appear to have strong and positive effects on child development, while the use of other electronics had negative effects on the development. The results further indicate that both use of and satisfaction with the functions of conversation, storytelling, and singing advanced child development across various dimensions, including physical, emotional, social, and academic. Policy and practice implications were discussed. Highlights • The literature shows that Chinese rural children have poorer child development than their urban peers. • This study validates the usage of and satisfaction with the intelligent robots have positive effects on child development. • The finding implies policy and practical implications to narrow the gap between urban and rural children. [ABSTRACT FROM AUTHOR]

The research and development of intelligent automation solutions is a ground-breaking point for the factory of the future. A promising and challenging mission is the use of autonomous robot systems to automate tasks in the field of maintenance. For this purpose, the robot system must be able to plan autonomously the different manipulation tasks and the corresponding paths. Basic requirements are the development of algorithms with a low computational complexity and the possibility to deal with environmental uncertainties. In this paper, an approach is presented, which is especially suited to solve the problem of maintenance automation. For this purpose, offline data from CAD is combined with online data from an RGBD vision system via a probabilistic filter, to compensate uncertainties from offline data. For planning the different tasks, a method is explained, which uses a symbolic description, founded on a novel sampling-based method to compute the disassembly space. For path planning, we use global state-of-the-art algorithms with a method that allows the adaption of the exploration stepsize in order to reduce the planning time. Every method is experimentally validated and discussed. Note to Practitioners—This paper was motivated by the current deficit to automate maintenance tasks in manufacturing systems. Today there exist many automatic methods for fault detection, but there is no possibility to restore the desired state. A typical application is the replacement of a defective component or service tasks such as refilling of cooling lubricant in machine tools. The focus of this paper is to provide automatic planning concepts for robot systems which deal with this problem. For this, a task planning algorithm is proposed which uses CAD and vision data. The advantage of this method is that it works on a contact analysis, based on a discrete approximation of the disassembly space, allowing a fast determination of possible robot manipulations. Also, a new preprocessing concept for path planning is developed which enables the adaptation of the stepsize of the path planner, based on the local occupancy of the environment. Through this, it is possible to drastically reduce the required planning effort. [ABSTRACT FROM AUTHOR]

This paper presents an algorithm called \varepsilon ^\star , for online coverage path planning of unknown environment. The algorithm is built upon the concept of an Exploratory Turing Machine (ETM), which acts as a supervisor to the autonomous vehicle to guide it with adaptive navigation commands. The ETM generates a coverage path online using Multiscale Adaptive Potential Surfaces (MAPS), which are hierarchically structured and dynamically updated based on sensor information. The \varepsilon ^\star -algorithm is computationally efficient, guarantees complete coverage, and does not suffer from the local extrema problem. Its performance is validated by 1) high-fidelity simulations on Player/Stage and 2) actual experiments in a laboratory setting on autonomous vehicles. [ABSTRACT FROM AUTHOR]

This study proposes the design of two efficient nature inspired intelligent optimal controllers, flower pollination algorithm (FPA) and bat algorithm (BA) for obtaining optimal path using an autonomous mobile robot in an unknown environment. FPA is based on the pollination process of flowering plants, which transfer pollens by using different pollinators. On the contrary, BA depends on echolocation and frequency tuning to solve different types of optimisation problems in engineering. To accomplish the path‐planning task of mobile robot autonomously, a fitness function has been introduced considering the distance between robot‐obstacle and robot‐goal to satisfy the conditions of obstacle avoidance and goal reaching behaviour of robot. Based on the values of objective function of the algorithms, mobile robot avoids obstacles in the unknown environment and moves towards the goal. In this work, the efficiency of such controllers is verified using some simulations in MATLAB environment. Further, an experimental work is carried out in real‐world environment using ARDUINO Mega 2560 microcontroller to ascertain the path length, travelling time and convergence speed of the two algorithms. [ABSTRACT FROM AUTHOR]

The early promise of the impact of machine intelligence did not involve the partitioning of the nascent field of Artificial Intelligence. The founders of AI envisioned the notion of embedded intelligence as being conjoined between perception, reasoning and actuation. Yet over the years the fields of AI and Robotics drifted apart. Practitioners of AI focused on problems and algorithms abstracted from the real world. Roboticists, generally with a background in mechanical and electrical engineering, concentrated on sensori-motor functions. That divergence is slowly being bridged with the maturity of both fields and with the growing interest in autonomous systems. This special issue brings together the state of the art and practice of the emergent field of integrated AI and Robotics, and highlights the key areas along which this current evolution of machine intelligence is heading. [ABSTRACT FROM AUTHOR]

An intelligent autonomous robot is supposed to collect information of the environment using camera(s) and/or sensor(s), analyze the collected data and plan its motion, execute the planned motion by avoiding collision with the obstacle (if any), on-line. Moreover, the robot should learn from its past experience. The principle of soft computing had been used in all the above areas of research by various researchers. A thorough literature review has been carried out in the present paper. Although a lot of studies had been reported in each of the above areas, there are scopes for further improvement. Some research issues have been identified for future study. [ABSTRACT FROM AUTHOR]

Mobile robots with complex morphology are essential for traversing rough terrains in Urban Search & Rescue missions. Since teleoperation of the complex morphology causes high cognitive load of the operator, the morphology is controlled autonomously. The autonomous control measures the robot state and surrounding terrain which is usually only partially observable, and thus the data are often incomplete. We marginalize the control over the missing measurements and evaluate an explicit safety condition. If the safety condition is violated, tactile terrain exploration by the body-mounted robotic arm gathers the missing data. [ABSTRACT FROM AUTHOR]

We present algorithms for environment exploration in the context of a habitat monitoring task, where the goal is to track radio-tagged invasive fish with autonomous surface or ground robots. The first task is navigation around an unknown obstacle using an input from a front-facing sonar. This capability is important for navigation on inland lakes, because plants and shallow shorelines are hard to map in advance. The second task involves energy harvesting for long-term operation. We address the problem of exploring the solar map of the environment which is used for energy-efficient navigation. For both problems, we present online algorithms and examine their performance using competitive analysis. In competitive analysis, the performance of an online algorithm is compared against the optimal offline algorithm. For obstacle avoidance, the offline algorithm knows the shape of the obstacle. For solar exploration, the offline algorithm knows the geometry of the shadow-casting objects. We obtain an $O(1)$ competitive ratio for obstacle avoidance and an $O(\log n)$ competitive ratio for solar exploration, where $n$ is the number of critical points to observe. The strategies for obstacle avoidance are validated through extensive field experiments, and the strategies for exploration are validated with simulations. [ABSTRACT FROM AUTHOR]

An intelligent wheelchair (IW) system is developed in order to support safe mobility for disabled or elderly people with various impairments. The proposed IW offers two main functions: obstacle detection and avoidance, and situation recognition. First, through a combination of a vision sensor and eight ultrasonic ones, it detects diverse obstacles and produces occupancy grid maps (OGMs) that describe environmental information, including the positions and sizes of obstacles, which is then given to the learning-based algorithm. By learning the common patterns among OGMs assigned to the same directions, the IW can automatically find paths to prevent collisions with obstacles. Second, it distinguishes a situation whereby the user is standing on a sidewalk, traffic intersection, or roadway through analyzing the texture and shape of the images, which aids in preventing any accidents that would result in fatal injuries to the user, such as collisions with vehicles. From the experiments that were performed in various environments, we can prove the following: (1) the proposed system can recognize different types of outdoor places with 98.3% accuracy; and (2) it can produce paths that avoid obstacles with 92.0% accuracy. [ABSTRACT FROM AUTHOR]

As a new technology, artificial intelligence (AI) has recently received increasing attention from researchers and has been successfully applied to many domains. Currently, the outbreak of the COVID-19 pandemic has not only put people's lives in jeopardy but has also interrupted social activities and stifled economic growth. Artificial intelligence, as the most cutting-edge science field, is critical in the fight against the pandemic. To respond scientifically to major emergencies like COVID-19, this article reviews the use of artificial intelligence in the combat against the pandemic from COVID-19 large data, intelligent devices and systems, and intelligent robots. This article's primary contributions are in two aspects: (1) we summarized the applications of AI in the pandemic, including virus spreading prediction, patient diagnosis, vaccine development, excluding potential virus carriers, telemedicine service, economic recovery, material distribution, disinfection, and health care. (2) We concluded the faced challenges during the AI-based pandemic prevention process, including multidimensional data, sub-intelligent algorithms, and unsystematic, and discussed corresponding solutions, such as 5G, cloud computing, and unsupervised learning algorithms. This article systematically surveyed the applications and challenges of AI technology during the pandemic, which is of great significance to promote the development of AI technology and can serve as a new reference for future emergencies. [ABSTRACT FROM AUTHOR]

Deployment of robots in practical domains poses key knowledge representation and reasoning challenges. Robots need to represent and reason with incomplete domain knowledge, acquiring and using sensor inputs based on need and availability. This paper presents an architecture that exploits the complementary strengths of declarative programming and probabilistic graphical models as a step toward addressing these challenges. Answer Set Prolog (ASP), a declarative language, is used to represent, and perform inference with, incomplete domain knowledge, including default information that holds in all but a few exceptional situations. A hierarchy of partially observable Markov decision processes (POMDPs) probabilistically models the uncertainty in sensor input processing and navigation. Nonmonotonic logical inference in ASP is used to generate a multinomial prior for probabilistic state estimation with the hierarchy of POMDPs. It is also used with historical data to construct a beta (meta) density model of priors for metareasoning and early termination of trials when appropriate. Robots equipped with this architecture automatically tailor sensor input processing and navigation to tasks at hand, revising existing knowledge using information extracted from sensor inputs. The architecture is empirically evaluated in simulation and on a mobile robot visually localizing objects in indoor domains. [ABSTRACT FROM PUBLISHER]

Creating robots that can act autonomously in dynamic unstructured environments requires dealing with novel objects. Thus, an offline learning phase is not sufficient for recognizing and manipulating such objects. Rather, an autonomous robot needs to acquire knowledge through its own interaction with its environment, without using heuristics encoding human insights about the domain. Interaction also allows information that is not present in static images of a scene to be elicited. Out of a potentially large set of possible interactions, a robot must select actions that are expected to have the most informative outcomes to learn efficiently. In the proposed bottom-up probabilistic approach, the robot achieves this goal by quantifying the expected informativeness of its own actions in information-theoretic terms. We use this approach to segment a scene into its constituent objects. We retain a probability distribution over segmentations. We show that this approach is robust in the presence of noise and uncertainty in real-world experiments. Evaluations show that the proposed information-theoretic approach allows a robot to efficiently determine the composite structure of its environment. We also show that our probabilistic model allows straightforward integration of multiple modalities, such as movement data and static scene features. Learned static scene features allow for experience from similar environments to speed up learning for new scenes. [ABSTRACT FROM AUTHOR]

Abstract: We present an approach for planning robotic manipulation tasks that uses a learned mapping between geometric states and logical predicates. Manipulation planning, because it requires task-level and geometric reasoning, requires such a mapping to convert between the two. Consider a robot tasked with putting several cups on a tray. The robot needs to find positions for all the objects, and may need to nest one cup inside another to get them all on the tray. This requires translating back and forth between symbolic states that the planner uses, such as stacked (cup1,cup2), and geometric states representing the positions and poses of the objects. We learn the mapping from labelled examples, and importantly learn a representation that can be used in both the forward (from geometric to symbolic) and reverse directions. This enables us to build symbolic representations of scenes the robot observes, but also to translate a desired symbolic state from a plan into a geometric state that the robot can achieve through manipulation. We also show how such a mapping can be used for efficient manipulation planning: the planner first plans symbolically, then applies the mapping to generate geometric positions that are then sent to a path planner. [Copyright &y& Elsevier]

With the rapid development of wireless communication technology and the growth in the popularity of the internet, intelligent robots can now be constructed that can replace the manpower needed to engage in dangerous work. Searching for ways to achieve humanoid robotic operation is a demanding undertaking. Practical robotic applications are difficult. Some common problems of robotic control are addressed in this work. We propose a method developed using the Motion Editor programming tool based on a graphical user interface. The results show that the Motion Editor method is able to control robotic movement well. [ABSTRACT FROM PUBLISHER]

A contest-oriented project for undergraduate students to learn implementation skills and theories related to intelligent mobile robots is presented in this paper. The project, related to Micromouse, Robotrace (Robotrace is the title of Taiwanese and Japanese robot races), and line-maze contests was developed by the embedded control system research group of the Department of Electronic Engineering, Lunghwa University of Science and Technology, Taiwan. It targets both those students who have to earn credits for a one-year special topics course and those who are just interested in making robots, and it is designed to motivate them to learn digital motion control, path planning, attitude correction, curvature detection and maze-solving algorithms. The students begin by getting acquainted with the development environment of microcontrollers, the characteristics of different sensors, and servomotor control techniques. Having learned these basic skills, they acquire further specific advanced skills and proceed to design their own mobile robots to compete in contests. The special topics course students' robots must pass examination by five teachers. Blogs and a wiki Web site for recording students' progress and experiences enhance the project's learning outcomes. Although not every student wins a prize in the contests, student feedback still shows that the contest-oriented project did motivate them to acquire the skills necessary to build and operate intelligent mobile robots. [ABSTRACT FROM PUBLISHER]

The task-based control framework is well established for its ability to generate complex behavior in versatile robots. When executing multiple complex tasks, continuous and stable transition among these tasks is one of the most important issues. In this paper, the problem of task transition is discussed to achieve continuous transitions between arbitrary tasks effectively. Instead of modifying the control laws, the design of intermediate desired values to be realized by existing controllers is proposed. The proposed approach can deal with arbitrary task sets, with or without priorities, for insertion and removal, and with priority rearrangement for hierarchical sets of tasks. The solution is generic and can be used for any type of transition. Two examples of uses include a time-driven transition to execute a given task schedule and a transition depending on the robot configuration to perform joint-limit avoidance behaviors. The performance of the algorithm is verified in simulations and on a physical robot. [ABSTRACT FROM AUTHOR]

Autism spectrum disorders are a group of lifelong disabilities that affect people's ability to communicate and to understand social cues. Research into applying robots as therapy tools has shown that robots seem to improve engagement and elicit novel social behaviors from people (particularly children and teenagers) with autism. Robot therapy for autism has been explored as one of the first application domains in the field of socially assistive robotics (SAR), which aims to develop robots that assist people with special needs through social interactions. In this review, we discuss the past decade's work in SAR systems designed for autism therapy by analyzing robot design decisions, human-robot interactions, and system evaluations. We conclude by discussing challenges and future trends for this young but rapidly developing research area. [ABSTRACT FROM AUTHOR]

This study introduces a method to enable a robot to learn how to perform new tasks through human demonstration and independent practice. The proposed process consists of two interconnected phases; in the first phase, state-action data are obtained from human demonstrations, and an aggregated state space is learned in terms of a decision tree that groups similar states together through reinforcement learning. Without the postprocess of trimming, in tree induction, the tree encodes a control policy that can be used to control the robot by means of repeatedly improving itself. Once a variety of behaviors is learned, more elaborate behaviors can be generated by selectively organizing several behaviors using another Q-learning algorithm. The composed outputs of the organized basic behaviors on the motor level are weighted using the policy learned through Q-learning. This approach uses three diverse Q-learning algorithms to learn complex behaviors. The experimental results show that the learned complicated behaviors, organized according to individual basic behaviors by the three Q-learning algorithms on different levels, can function more adaptively in a dynamic environment. [ABSTRACT FROM PUBLISHER]

Powered wheelchair users often struggle to drive safely and effectively and, in more critical cases, can only get around when accompanied by an assistant. To address these issues, we propose a collaborative control mechanism that assists users as and when they require help. The system uses a multiple-hypothesis method to predict the driver's intentions and, if necessary, adjusts the control signals to achieve the desired goal safely. The main emphasis of this paper is on a comprehensive evaluation, where we not only look at the system performance but also, perhaps more importantly, characterize the user performance in an experiment that combines eye tracking with a secondary task. Without assistance, participants experienced multiple collisions while driving around the predefined route. Conversely, when they were assisted by the collaborative controller, not only did they drive more safely but also they were able to pay less attention to their driving, resulting in a reduced cognitive workload. We discuss the importance of these results and their implications for other applications of shared control, such as brain–machine interfaces, where it could be used to compensate for both the low frequency and the low resolution of the user input. [ABSTRACT FROM AUTHOR]

We propose distributed algorithms to automatically deploy a team of mobile robots to partition and provide coverage of a nonconvex environment. To handle arbitrary nonconvex environments, we represent them as graphs. Our partitioning and coverage algorithm requires only short-range, unreliable pairwise “gossip” communication. The algorithm has two components: 1) a motion protocol to ensure that neighboring robots communicate at least sporadically and 2) a pairwise partitioning rule to update territory ownership when two robots communicate. By studying an appropriate dynamical system on the space of partitions of the graph vertices, we prove that territory ownership converges to a pairwise-optimal partition in finite time. This new equilibrium set represents improved performance over common Lloyd-type algorithms. Additionally, our algorithm is an ``anytime algorithm'' that also scales well for large teams and can be run by on-board computers with limited resources. Finally, we report on large-scale simulations in complex environments and hardware experiments using the Player/Stage robot control system. [ABSTRACT FROM PUBLISHER]

The aim of this paper is to improve the skills of robotic systems in their interaction with nearby objects. The basic idea is to enhance visual estimation of objects in the world through the merging of different visual estimators of the same stimuli. A neuroscience-inspired model of stereoptic and perspective orientation estimators, merged according to different criteria, is implemented on a robotic setup and tested in different conditions. Experimental results suggest that the integration of multiple monocular and binocular cues can make robot sensory systems more reliable and versatile. The same results, compared with simulations and data from human studies, show that the model is able to reproduce some well-recognized neuropsychological effects. [ABSTRACT FROM AUTHOR]

Robots are frequently presented with vast arrays of diverse data. Unfortunately, perfect memory and recall provides a mixed blessing. While flawless recollection of episodic data allows increased reasoning, photographic memory can hinder a robot's ability to operate in real-time dynamic environments. Human-inspired forgetting methods may enable robotic systems to rid themselves of out-dated, irrelevant, and erroneous data. This paper presents the use of human-inspired forgetting to act as a filter, removing unnecessary, erroneous, and out-of-date information. The novel ActSimple forgetting algorithm has been developed specifically to provide effective forgetting capabilities to robotic systems. This paper presents the ActSimple algorithm and how it was optimized and tested in a WiFi signal strength estimation task. The results generated by real-world testing suggest that human-inspired forgetting is an effective means of improving the ability of mobile robots to move and operate within complex and dynamic environments. [ABSTRACT FROM AUTHOR]

We present here a framework for the generation, application, and assessment of assistive models for the purpose of aiding automated robotic parameter optimization methods. Our approach represents an expansion of traditional machine learning implementations by employing models to predict the performances of input parameter sequences and then filter a potential population of inputs prior to evaluation on a physical system. We further provide a basis for numerically qualifying these models to determine whether or not they are of sufficient quality to be capable of fulfilling their predictive responsibilities. We demonstrate the effectiveness of this approach using an industrial robotic testbed on a variety of mechanical assemblies, each requiring a different strategy for completion. [ABSTRACT FROM AUTHOR]

This paper describes a relative localization system used to achieve the navigation of a convoy of robotic units in indoor environments. This positioning system is carried out fusing two sensorial sources: (a) an odometric system and (b) a laser scanner together with artificial landmarks located on top of the units. The laser source allows one to compensate the cumulative error inherent to dead-reckoning; whereas the odometry source provides less pose uncertainty in short trajectories. A discrete Extended Kalman Filter, customized for this application, is used in order to accomplish this aim under real time constraints. Different experimental results with a convoy of Pioneer P3-DX units tracking non-linear trajectories are shown. The paper shows that a simple setup based on low cost laser range systems and robot built-in odometry sensors is able to give a high degree of robustness and accuracy to the relative localization problem of convoy units for indoor applications. [ABSTRACT FROM AUTHOR]

Dynamic tactile sensing is a fundamental ability to recognize materials and objects. However, while humans are born with partially developed dynamic tactile sensing and quickly master this skill, today’s robots remain in their infancy. The development of such a sense requires not only better sensors but the right algorithms to deal with these sensors’ data as well. For example, when classifying a material based on touch, the data are noisy, high-dimensional, and contain irrelevant signals as well as essential ones. Few classification methods from machine learning can deal with such problems. In this paper, we propose an efficient approach to infer suitable lower dimensional representations of the tactile data. In order to classify materials based on only the sense of touch, these representations are autonomously discovered using visual information of the surfaces during training. However, accurately pairing vision and tactile samples in real-robot applications is a difficult problem. The proposed approach, therefore, works with weak pairings between the modalities. Experiments show that the resulting approach is very robust and yields significantly higher classification performance based on only dynamic tactile sensing. [ABSTRACT FROM AUTHOR]

Voice-based interaction generates a lot of interest for natural human–robot interaction. Auditory information from microphones is an essential clue for a robot's attention to a person. Fault detection of microphones is required in order to improve the reliability of the voice-based human–robot interaction. This paper proposes a new approach for real-time fault detection in a microphone array in conversation without a calibration signal and a known sound source position by the intercorrelation of features in voice activity detection. The approach is successfully applied to a six-microphone system, and experimental results show an average fault detection of 97.6%. [ABSTRACT FROM AUTHOR]

This paper presents a physical model developed to find the directions of forces and moments required to open a plastic bag-which forces will contribute toward opening the knot and which forces will lock it further. The analysis is part of the implementation of a Q(A)-learning algorithm on a robot system. The learning task is to let a fixed-arm robot observe the position of a plastic bag located on a platform, grasp it, and learn how to shake out its contents in minimum time. The physical model proves that the learned optimal bag shaking policy is consistent with the physical model and shows that there were no subjective influences. Experimental results show that the learned policy actually converged to the best policy. Note to Practitioners-This paper was motivated by the problem of developing a physical model capable of finding the directions of force and moments required to open a knotted plastic bag-which forces will contribute toward opening the knot and which forces will lock it further. The analysis was part of the implementation of a reinforcement-learning algorithm for a fixed-arm robot system called "RoboShake." The learning task was to observe the position of a bag contains suspicious items (such as biological or chemical items), grasp it, and learn the optimal sequence of motions to shake out its contents in minimum time. An extremely interesting finding was the fact that the robot's optimal policy always converged to a one that was consistent with Newton's three laws of motion. The system learned the optimal policies by interaction with the environment without using these laws as part of the system design/implementation. From a philosophical aspect, the experimental robotic platform found solutions that were consistent with Newton's laws of motion, i.e., the system was capable of explaining Newton's laws of motion without being "aware" of them, and in a way, and to "discover" the existence of the second and the third laws. Experimental systems of this kind, especially if designed with a capability to carry out new experiments independently, may help to explain, discover, or predict laws/rules (not necessarily of machines/robots) that are not known to us yet. [ABSTRACT FROM AUTHOR]

For the last decade, we have been developing a vision-based architecture for mobile robot navigation. Using our bio- inspired model of navigation, robots can perform sensory-motor tasks in real time in unknown indoor as well as outdoor environments. We address here the problem of autonomous incremental learning of a sensory-motor task, demonstrated by an operator guiding a robot. The proposed system allows for semisupervision of task learning and is able to adapt the environmental partitioning to the complexity of the desired behavior. A real dialogue based on actions emerges from the interactive teaching. The interaction leads the robot to autonomously build a precise sensory-motor dynamics that approximates the behavior of the teacher. The usability of the system is highlighted by experiments on real robots, in both indoor and outdoor environments. Accuracy measures are also proposed in order to evaluate the learned behavior as compared to the expected behavioral attractor. These measures, used first in a real experiment and then in a simulated experiment, demonstrate how a real interaction between the teacher and the robot influences the learning process. [ABSTRACT FROM AUTHOR]

The purpose of this study is to develop an interactive and emotional robot that is designed with passive characteristics and a delicate interaction concept using the interaction response: 'Bruises and complexion color due to emotional stimuli'. In order to overcome the mismatch of cue realism issues that cause the Uncanny Valley, an emotional interaction robot, Mung, was developed with a simple design composed of a body and two eyes. Mung could recognize human emotions from human–robot or human–human verbal communications. The developed robot expresses its emotions according to its emotional state modeled by a mass-spring-damper system with an elastic-hysteresis spring. The robot displays a bruise when it is in a negative emotional state, the same as a human becomes bruised when being physically hurt, and the robot shows a natural complexion when its emotional wound is removed. The effectiveness of the emotional expression using color with the concepts of bruising and complexion colors is qualified, and the feasibility of the developed robot was tested in several exhibitions and field trials. [ABSTRACT FROM AUTHOR]

Abstract: This paper involves a collision free target tracking problem of multi-agent robot system. Target tracking requires team coordination to maintain a desired formation and to keep team-mates and target together. Every team-mate makes decisions on their moving direction that may spoil the tactical position of others and makes the global coordination task nontrivial. The contribution of the paper is twofold. First, the convergence of target tracking is improved by a new game theoretic concept using a semi-cooperative Stackelberg equilibrium point and a new formation component in the individual cost functions. To enhance the robustness, a PD like fuzzy controller tunes the cost function weights directly for the game theoretic solution and helps to achieve a prescribed value of cost function components. Simulation result for target tracking by a three-member robot team is presented. [Copyright &y& Elsevier]

For autonomous driving research, using a scaled vehicle platform is a viable alternative compared to a full-scale vehicle. However, using embedded solutions such as small robotic platforms with differential driving or radio-controlled (RC) car-based platforms can be limiting on, for example, sensor package restrictions or computing challenges. Furthermore, for a given controller, specialized expertise and abilities are necessary. To address such problems, this paper proposes a feasible solution, the Ridon vehicle, which is a spacious ride-on automobile with high-driving electric power and a custom-designed drive-by-wire system powered by a full-scale machine-learning-ready computer. The major objective of this paper is to provide a thorough and appropriate method for constructing a cost-effective platform with a drive-by-wire system and sensor packages so that machine-learning-based algorithms can be tested and deployed on a scaled vehicle. The proposed platform employs a modular and hierarchical software architecture, with microcontroller programs handling the low-level motor controls and a graphics processing unit (GPU)-powered laptop computer processing the higher and more sophisticated algorithms. The Ridon vehicle platform is validated by employing it in a deep-learning-based behavioral cloning study. The suggested platform's affordability and adaptability would benefit broader research and the education community. [ABSTRACT FROM AUTHOR]

We present a system for robust robot skill acquisition from kinesthetic demonstrations. This system allows a robot to learn a simple goal-directed gesture and correctly reproduce it despite changes in the initial conditions and perturbations in the environment. It combines a dynamical system control approach with tools of statistical learning theory and provides a solution to the inverse kinematics problem when dealing with a redundant manipulator. The system is validated on two experiments involving a humanoid robot: putting an object into a box and reaching for and grasping an object. [ABSTRACT FROM AUTHOR]

Abstract: The proposed approach in this paper involves a new grid-based map model called “memory grid” and a new behavior-based navigation method called “minimum risk method”. The memory grid map records not only the environmental information, but also the robot experience. The minimum risk method is just one of the applications of the memory grid technique, which addresses the local minimum problem faced by a goal-oriented robot navigating in unknown indoor environments. The Minimum Risk implies that the robot is able to choose the safest region that can avoid colliding with obstacles and prevent the robot from iterating previous trajectory. This method is demonstrated to work in long wall, large concave, recursive -shaped, unstructured, cluttered, maze-like, and dynamic indoor environments. It adopts a strategy of multi-behavior coordination in which a novel path-searching behavior is developed to recommend the region offering the minimum risk. Fuzzy logic is used to implement the behavior design and coordination. The proposed approach is verified with simulation and real-world tests. [Copyright &y& Elsevier]

This paper focuses on the development of control algorithms for intelligent robotic tools that assist off-pump coronary artery bypass graft (CABG) surgery. In the robotic-assisted CABG surgery, the surgeon operates on the beating heart using intelligent robotic instruments. Robotic tools actively cancel the relative motion between the surgical instruments and the point of interest on the beating heart, dynamically stabilizing the heart for the operation. This algorithm is called active relative motion canceling (ARMC). Here, a model-based intelligent ARMC algorithm employing biological signals, such as electrocardiogram, to achieve effective motion cancellation is proposed. Finally, experimental results of the algorithm on a 3-degree-of-freedom robotic test-bed system are reported. [ABSTRACT FROM PUBLISHER]

In this paper, we focus on using pointing behavior for a natural interface. Recently, robots that fit into the human community have been needed. A gestural interface is important for use with these robots. Gestural recognition has already been studied for use in a human-machine interface. We investigated to find a system that recognizes users' intentions by using their gestural information in particular situations. Therefore, we constructed a system based on interpersonal communication that uses pointing gestures as information. Our aim is not to calculate accurately the point indicated by a user. The system recognizes objects or the pointing direction and decides its own action by automatically considering context. In addition, we report three experiments with this pointing behavior. In the first experiment, a user instructed the robot to follow a specified route. In the second, the user moved a robot or an object by using a virtual room interface. In the third, the user pointed out a specific parking space in which the system was to park a car. [ABSTRACT FROM AUTHOR]

This paper discusses a perceptual system for natural communication between a partner robot and a human. The prediction is very important to reduce the computational cost and to extract the perceptual information for the natural communication with a human in the future. First, we propose a prediction-based perceptual control system based on spiking neurons. The proposed method is composed of four layers, namely: 1) input layer; 2) clustering layer; 3) prediction layer; and 4) perceptual module selection layer. Next, we propose an unsupervised learning method to perform the clustering of human behavior patterns. Furthermore, the robot selects perceptual modules used in the next perception according to the predicted perceptual mode. Furthermore, we show several experimental results of the communication between a partner robot and a human based on our proposed method. [ABSTRACT FROM AUTHOR]