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Compared with traditional methods relying on manual teaching or system modeling, data-driven learning methods, such as deep reinforcement learning and imitation learning, show more promising potential to cope with the challenges brought by increasingly complex tasks and environments, which have become the hot research topic in the field of robot skill learning. However, the contradiction between the difficulty of collecting robot–environment interaction data and the low data efficiency causes all these methods to face a serious data dilemma, which has become one of the key issues restricting their development. Therefore, this paper aims to comprehensively sort out and analyze the cause and solutions for the data dilemma in robot skill learning.

First, this review analyzes the causes of the data dilemma based on the classification and comparison of data-driven methods for robot skill learning; Then, the existing methods used to solve the data dilemma are introduced in detail. Finally, this review discusses the remaining open challenges and promising research topics for solving the data dilemma in the future.

This review shows that simulation–reality combination, state representation learning and knowledge sharing are crucial for overcoming the data dilemma of robot skill learning.

To the best of the authors’ knowledge, there are no surveys that systematically and comprehensively sort out and analyze the data dilemma in robot skill learning in the existing literature. It is hoped that this review can be helpful to better address the data dilemma in robot skill learning in the future.

The purpose of this paper is to provide an insight into the present-day state of bin picking by considering research, technology, products and applications.

Following a short introduction, this first provides examples of recent bin picking research. It then discusses a selection of commercial product developments and applications. Finally, brief conclusions are drawn.

Bin picking has the potential to eliminate repetitive, manual part handling practices in many sectors of the manufacturing and logistics industries. Systems combine robotic gripping and manipulation with machine vision and specialist software and tend to be complex to install and commission. They are produced by robot manufacturers, system integrators, software developers and machine vision specialists and all are constantly developing and improving the technology. These developments are supported by a strong academic research effort, much involving artificial intelligence methods, and while the technology is evolving rapidly, it is yet to reach the point where deployments are routine and widespread.

This provides a timely review of recent bin picking research and commercial developments.

Because of the advantages of high deposition efficiency and low manufacturing cost compared with other additive technologies, robotic wire arc additive manufacturing (WAAM) technology has been widely applied for fabricating medium- to large-scale metallic components. The additive manufacturing (AM) method is a relatively complex process, which involves the workpiece modeling, conversion of the model file, slicing, path planning and so on. Then the structure is formed by the accumulated weld bead. However, the poor forming accuracy of WAAM usually leads to severe dimensional deviation between the as-built and the predesigned structures. This paper aims to propose a visual sensing technology and deep learning–assisted WAAM method for fabricating metallic structure, to simplify the complex WAAM process and improve the forming accuracy.

Instead of slicing of the workpiece modeling and generating all the welding torch paths in advance of the fabricating process, this method is carried out by adding the feature point regression branch into the Yolov5 algorithm, to detect the feature point from the images of the as-built structure. The coordinates of the feature points of each deposition layer can be calculated automatically. Then the welding torch trajectory for the next deposition layer is generated based on the position of feature point.

The mean average precision score of modified YOLOv5 detector is 99.5%. Two types of overhanging structures have been fabricated by the proposed method. The center contour error between the actual and theoretical is 0.56 and 0.27 mm in width direction, and 0.43 and 0.23 mm in height direction, respectively.

The fabrication of circular overhanging structures without using the complicate slicing strategy, turning table or other extra support verified the possibility of the robotic WAAM system with deep learning technology.

Because of the key role of joint torque in industrial robots (IRs) motion performance control and energy consumption calculation and efficiency optimization, the purpose of this paper is to propose a deep learning torque prediction method based on long short-term memory (LSTM) recurrent neural networks optimized by particle swarm optimization (PSO), which can accurately predict the the joint torque.

The proposed model optimized the LSTM with PSO algorithm to accurately predict the IRs joint torque. The authors design an excitation trajectory for ABB 1600–10/145 experimental robot and collect its relative dynamic data. The LSTM model was trained with the experimental data, and PSO was used to find optimal number of LSTM nodes and learning rate, then a torque prediction model is established based on PSO-LSTM deep learning method. The novel model is used to predict the robot’s six joint torque and the root mean error squares of the predicted data together with least squares (LS) method were comparably studied.

The predicted joint torque value by PSO-LSTM deep learning approach is highly overlapped with those from real experiment robot, and the error is quite small. The average square error between the predicted joint torque data and experiment data is 2.31 N.m smaller than that with the LS method. The accuracy of the novel PSO-LSTM learning method for joint torque prediction of IR is proved.

PSO and LSTM model are deeply integrated for the first time to predict the joint torque of IR and the prediction accuracy is verified.

In traditional calibration methods of kinematics parameters of industrial robots, dozens of model parameters are identified together based on an optimization procedure. Due to different contributions of model parameter errors to the tool center point positioning error of industrial robots, obtaining good results for all model parameters is very difficult. Therefore, the purpose of this paper is to propose a sequential calibration method specifically for transmission ratio parameters, which includes reduction ratios and coupling ratios of industrial robot joints.

The ABB IRB 1410 industrial robot is considered as an example in this study. The transmission ratios for each joint of the robot are identified using the spatial circle fitting method based on spatial vectors, which fit the center and radius of joint rotation with the least squares optimization algorithm. In addition, a method based on the Rodrigues’ formula is designed and presented for identifying the actual coupling ratio of the robot. Subsequently, an experiment is carried out to verify the proposed sequential calibration method of transmission ratios.

In this experiment, the actual positions of the linkages before and after joint rotations are measured by a laser tracker. Accurate results of the reduction ratios and the coupling ratios are calculated, and the results are verified experimentally. The results show that by calibrating the reduction ratios and coupling ratios of the ABB robot, the rotation angle errors of the robot joints can be reduced.

The authors propose a sequential calibration method for transmission ratio parameters, including reduction ratios and coupling ratios of industrial robot joints. An experiment is carried out to verify this proposed sequential calibration method. This study may be beneficial for calibrating the kinematic parameters of industrial robots and improving their positioning accuracy.

Shortly before the Second World War, a woman who had never accepted her orphan status, Gabrielle Bonheur Chanel, nicknamed ‘Little Coco’ by her father and known as ‘Coco’ to her relatives, became the first women in history to build a world-class industrial empire. By 1935, Coco, a fashion designer and industry captain, was employing more than 4,000 workers and had sold more than 28,000 dresses, tailored jackets and women's suits. Born into a poor family and raised in an orphanage, she enjoyed an intense social life in Paris in the 1920s, rubbing shoulders with artists, creators and the rising stars of her time.

Thanks to her entrepreneurial skills, she was able to innovate in her methods and in her trendsetting approach to fashion design and promotion. Coco Chanel was committed and creative, had the soul of an entrepreneur and went on to become a world leader in a brand new sector combining fashion, accessories and perfumes that she would help shape. By the end of her life, she had redefined French elegance and revolutionized the way people dressed.

The purpose of the research is to substantiate the possibilities of the development of ecological design in the process of future designers professional training and the implementation of student projects in practice.

This article offers one of the possible ways to solve the problem of rational use of wastes of natural resources in the projects of design students. In Ukraine, the ecological direction acquires national features and many Ukrainian manufacturers associate this mainly with ecologically clean materials: this is most typical for furniture products and traditional construction made of wood. The proposed technique is an effective tool for analyzing the interaction of environmental and design disciplines at different levels, as well as an effective criterion for evaluating methods of structuring educational material. The introduction of the correlation index makes assessing the effectiveness of the application of environmental knowledge in design possible.

The analysis of the world ecological design experience makes presenting the Ukrainian experience of environmental design possible, the origins of which date back to the 1920s of the 20th century. In terms of the purpose and methods of education, the authors consider art and industrial schools to be the primary basis of design education in Ukraine. In the 1990s of the 20th century, on the material and technical bases, educational-methodical and staffing with experienced teachers that design departments are formed, which train and graduate certified designers in various specializations. The need for readiness to solve the tasks of environmental education of future designers in the context of end-to-end environmental training is an urgent problem. Relying on ecological concepts makes optimally structuring the educational material possible, taking into account the requirements for both environmental and professional training.

Studies have established that the application of the authors' method of continuous environmental education has a positive effect on the assimilation of environmental knowledge and method's use in professional theory. Increasing the rates of assimilation and application of environmental knowledge by students has a positive effect on the interaction of environmental and professional knowledge.

The practical significance of the project consists in the implementation of the development of design projects on specific topics, including competitive, diploma and master's theses. The presented examples of designer shaping from wastes are expressive examples to follow in educational design, important for the development of students' creative and ecological thinking. The methodology is tested in several institutions of higher education in Ukraine, and the authors hope that this can spread in other countries, in particular in the training of designers specializing in “Furniture Design.”

Ecodesign is gradually being established in the general structure of design theory and, accordingly, has the right to an independent role in the formation of new principles of shaping the spatial and object environment of human life.

New approaches require new educational standards, curricula and teaching methods, directed in particular to the development of a systemic interdisciplinary approach.

The purpose of this paper is to explore the nuances of the consequences of greenwashing in the consumer and financial markets. Greenwashing is discussed frequently but in very abstract terms. Hence, a closer examination of the palpable consequences elucidates the ripple effects of this widespread phenomenon.

Focal points are the concept of green marketing, the stigmatization of corporations in the media and the regulatory consequences of greenwashing behaviour across consumer and financial markets. The two markets are paralleled in order to trace the novelties as well as the points of commonality in greenwashing.

The current consequences are an insufficient deterrence in both markets. The regulatory trend in both markets is leaning towards more stringent and punitive measures, which will likely affect the efficacy of the deterrence factor.

The influence on consumer perception is identified both as a motivating factor for greenwashing and as one of the most immediate elements which is negatively influenced by its exposition. In addition to the fact that greenwashing practices are common across the two markets, this paper identifies that a systemic deterioration of investor trusts significantly compromises the potential of sustainable finance and impacts investment in the financial market, mirrored in the negative consequences on consumer reactions to greenwashed products.

The student will examine from a systemic perspective qualitative information from a company to propose improvements to its business model.

After working for more than 10 years with a global company, in January 2006 the Guadalajara-based jewelry SME, Divine Jewelry Co. (DJC), was facing liquidity problems, overstock and a debt from a bank loan, among others. The planned expansion of DJC, a family business founded in 1980, had resulted in just the opposite. Daniel, the owner and CEO of DJC, was determined to reverse the company's precarious situation as soon as possible. Some of the questions he asked himself centered on what actions he should take to pay off liabilities and try to survive in the industry his business had held a place in for 25 years. Daniel wondered whether, to pay off debts and have sufficient liquidity to have working capital and move forward, it would be enough to make efforts to recover those clients they had stopped serving, along with getting new ones. In addition, he thought it was necessary to formulate a plan to use the remaining proceeds from the sale of the property if they had to dispose of it. The case is about analyzing the strategic management of a company, specifically its business model, considering the context of its industry. The case also illustrates the problems that arise from focusing on serving a single client.

The case “Divine Jewelry Co: From Expansion to Survival” has been designed to be used by university students in initial management or strategic management courses on the following topics: Business modelsPorter's Five Forces ModelFormulation of strategies

Teaching notes are available for educators only.

CSS11: Strategy.

This study aims to qualitatively examine the relationship between the indoor environmental quality (IEQ), lecturers’ and students’ perceived internal responses and academic performance.

To capture user experiences with the IEQ in classrooms, semi-structured interviews with 11 lecturers and three focus group discussions with 24 students were conducted, transcribed, coded and analyzed using direct content analysis.

The findings show that lecturers and students experience poor thermal, lighting, acoustic and indoor air quality (IAQ) conditions that may influence their ability to teach and learn. Maintaining acceptable thermal and IAQ conditions was difficult for lecturers, as opening windows or doors caused noise disturbances. In uncomfortable conditions, lecturers may decide to give a break earlier or shorten a lecture. When students experienced discomfort, it may affect their ability to concentrate, their emotional status and their quality of learning.

The findings originate from a relatively small sample, which might have limited the number and variety of identified associations between environment and users.

Maintaining acceptable air and thermal conditions will mitigate the need to open windows and doors. Keeping doors and windows closed will prevent noise disturbances and related distractions. This will support the quality of learning in classrooms. This study reveals the end users’ perspectives and preferences, which can inspire designers of new school buildings in higher education.

This study emphasizes the importance of creating and maintaining optimal IEQ conditions to support the quality of teaching and learning. These conditions are particularly relevant when classroom occupancy rates are high or outdoor conditions are unfavourable.