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Imitation learning is a powerful tool for planning the trajectory of robotic end-effectors in Cartesian space. Present methods can adapt the trajectory to the obstacle; however, the solutions may not always satisfy users, whereas it is hard for a nonexpert user to teach the robot to avoid obstacles in time as he/she wishes through demonstrations. This paper aims to address the above problem by proposing an approach that combines human supervision with the kernelized movement primitives (KMP) model.

This approach first extracts the reference database used to train KMP from demonstrations by using Gaussian mixture model and Gaussian mixture regression. Subsequently, KMP is used to modulate the trajectory of robotic end-effectors in real time based on feedback from its interaction with humans to avoid obstacles, which benefits from a novel reference database update strategy. The user can test different obstacle avoidance trajectories in the current task until a satisfactory solution is found.

Experiments performed with the KUKA cobot for obstacle avoidance show that this approach can adapt the trajectories of the robotic end-effector to the user’s wishes in real time, including trajectories that the robot has already passed and has not yet passed. Simulation comparisons also show that it exhibits better performance than KMP with the original reference database update strategy.

An interactive learning approach based on KMP is proposed and verified, which not only enables users to plan the trajectory of robotic end-effectors for obstacle avoidance more conveniently and efficiently but also provides an effective idea for accomplishing interactive learning tasks under constraints.

In order to deepen understanding of team processes in dynamic organizational contexts, we suggest that analyses employing techniques to identify and analyze team member interaction patterns and trajectories are necessary. After presenting a brief review of interaction data coding and reliability requirements, we first review examples of two approaches used in the identification and analysis of interaction patterns in teams: lag sequential analysis and T-pattern analysis. We then describe and discuss three statistical techniques used to analyze team interaction trajectories: random coefficient modeling, latent growth modeling, and discontinuous growth analysis. We close by suggesting several ways in which these techniques could be applied to data analysis in order to expand our knowledge of team interaction, processes, and outcomes in complex and dynamic settings.

Trajectory tracking is an important issue to underactuated unmanned surface vehicles (USVs). However, parametric uncertainties and environmental disturbances bring great challenges to the precise trajectory tracking control of USVs. This paper aims to propose a robust trajectory tracking control algorithm with exponential stability for underactuated USVs with parametric uncertainties and unknown environmental disturbances.

In this method, the backstepping method and sliding mode control method are combined to ensure that the underactuated USV can track and maintain the desired trajectory. In addition, a modified switching-gain adaptation algorithm is adopted to enhance the robustness and reduce chattering. Besides, the global exponential stability of the closed-loop system is proved by Lyapunov’s direct method.

The proposed method in this paper offers a robust trajectory tracking solution to underactuated USVs and it is verified by simulations and experiments. Compared with the traditional proportion-integral-derivative method and several state-of-the-art algorithms, the proposed method has superior performance in simulation and experimental results.

This paper proposes a robust trajectory tracking control algorithm with exponential stability for underactuated USVs. The proposed method achieves exponential stability with better robustness and transient performance.

The purpose of this paper is to propose a new algorithm based on programming by demonstration and exception strategies to solve assembly tasks such as peg-in-hole.

Data describing the demonstrated tasks are obtained by kinesthetic guiding. The demonstrated trajectories are transferred to new robot workspaces using three-dimensional (3D) vision. Noise introduced by vision when transferring the task to a new configuration could cause the execution to fail, but such problems are resolved through exception strategies.

This paper demonstrated that the proposed approach combined with exception strategies outperforms traditional approaches for robot-based assembly. Experimental evaluation was carried out on Cranfield Benchmark, which constitutes a standardized assembly task in robotics. This paper also performed statistical evaluation based on experiments carried out on two different robotic platforms.

The developed framework can have an important impact for robot assembly processes, which are among the most important applications of industrial robots. Our future plans involve implementation of our framework in a commercially available robot controller.

This paper proposes a new approach to the robot assembly based on the Learning by Demonstration (LbD) paradigm. The proposed framework enables to quickly program new assembly tasks without the need for detailed analysis of the geometric and dynamic characteristics of workpieces involved in the assembly task. The algorithm provides an effective disturbance rejection, improved stability and increased overall performance. The proposed exception strategies increase the success rate of the algorithm when the task is transferred to new areas of the workspace, where it is necessary to deal with vision noise and altered dynamic characteristics of the task.

Teaching by demonstration (TbD) is a promising way for robot learning skills in human and robot collaborative hybrid manufacturing lines. Traditionally, TbD systems have only concentrated on how to enable robots to learn movement skills from humans. This paper aims to develop an extended TbD system which can also enable learning stiffness regulation strategies from humans.

Here, the authors propose an extended dynamical motor primitives (DMP) framework to achieve this goal. In addition to the advantages of the traditional ones, the authors’ framework can enable robots to simultaneously learn stiffness and the movement from human demonstrations. Additionally, Gaussian mixture model (GMM) is used to capture the features of movement and of stiffness from multiple demonstrations of the same skill. Human limb surface electromyography (sEMG) signals are estimated to obtain the reference stiffness profiles.

The authors have experimentally demonstrated the effectiveness of the proposed framework. It shows that the authors approach could allow the robot to execute tasks in a variable impedance control mode with the learned movement trajectories and stiffness profiles.

In robot skill acquisition, DMP is widely used to encode robotic behaviors. So far, however, these DMP modes do not provide the ability to properly represent and generalize stiffness profiles. The authors argue that both movement trajectories and stiffness profiles should be considered equally in robot skill learning. The authors’ approach has great potential of applications in the future hybrid manufacturing lines.

This study aims to deal with the case adaptation problem associated with continuous data by providing a non-zero base solution for knowledge users in solving a given situation.

Firstly, the neighbourhood transformation of the initial case base and the view similarity between the problem and the existing cases will be examined. Multiple cases with perspective similarity or above a predefined threshold will be used as the adaption cases. Secondly, on the decision rule set of the decision space, the deterministic decision model of the corresponding distance between the problem and the set of lower approximate objects under each choice class of the adaptation set is applied to extract the decision rule set of the case condition space. Finally, the solution elements of the problem will be reconstructed using the rule set and the values of the problem's conditional elements.

The findings suggest that the classic knowledge matching approach reveals the user with the most similar knowledge/cases but relatively low satisfaction. This also revealed a non-zero adaptation based on human–computer interaction, which has the difficulties of solid subjectivity and low adaptation efficiency.

In this study the multi-case inductive adaptation of the problem to be solved is carried out by analyzing and extracting the law of the effect of the centralized conditions on the decision-making of the adaptation. The adaption process is more rigorous with less subjective influence better reliability and higher application value. The approach described in this research can directly change the original data set which is more beneficial to enhancing problem-solving accuracy while broadening the application area of the adaptation mechanism.

The examination of the calculation cases confirms the innovation of this study in comparison to the traditional method of matching cases with tacit knowledge extrapolation.

The algorithm models established in this study develop theoretical directions for a multi-case induction adaptation study of tacit knowledge.

This study designs a multi-case induction adaptation scheme by combining NRS and CBR for implicitly knowledgeable exogenous cases. A game-theoretic combinatorial assignment method is applied to calculate the case view and the view similarity based on the threshold screening.

The purpose of this paper is to explain how trajectory management in hospitals is challenged by the introduction of accelerated discharge schemes. The patient trajectory is formed by short stays within health-care organizations, which requires a substantial effort for professionals to be successful in clarifying each patient's medical situation. The patients, at the same time, often have complicated illness stories, and professionals only see a limited part of the patient's trajectory.

This paper is based on extensive ethnographic studies in a newly established cardiac day unit introducing same-day discharge schemes for patients with ischemic and arrhythmic heart disease.

The findings demonstrate that the patient trajectory becomes a “temporal patient trajectory” and encounters a short-term reality, where tensions arise between admission time and the trajectory as a whole. In managing temporal patient trajectories, formal organizing and patient experiences intersect in events that emerge from conversations and span past, present and future in relation to patient treatment. Professionals engage in articulation work to maintain coherence by allowing patients to hold different events together over time.

The paper provides new insights into the challenges of managing trajectories in same-day discharge schemes where the pressure to move quickly and ensure patient discharge is intense. The paper offers a novel theoretical perspective on trajectory management as an ongoing temporal process. The analysis displays temporal tensions between patient experiences and the accelerated discharge scheme and how professionals manage to overcome these tensions by bridging the patient's long illness story and the short trajectory within the cardiac day unit.

The purpose of the paper is to determine the specifics of scientific discourse in the sphere of managing professional mobility of the Russian youth and to develop a cognitive scheme of sociological research of this problem.

The authors use the potential of the stratification and activity approaches, the theory of multiplicity of professionalization, and multidimensional analysis as the most perspective method for sociological measuring of phenomena and processes in the professional sphere in the conditions of society's transformation.

The authors determine and study the key directions in the sphere of studying the professional mobility of the Russian youth and managing this process; substantiate the conceptual and methodological underdevelopment of this problem and offer the proprietary methodological strategy of its sociological study through distinguishing three stages of professional development of youth; substantiate the perspectives of its usage in studying the professional mobility of youth and constructing the effective policy of managing this process; outline the role of state and education in overcoming crisis factors of professional development of youth in the context of domination in the modern world and among the Russian youth.

Importance of this research is predetermined by the need for theoretical consideration of top-priority directions in studying professional mobility of youth and the means of managing this process in view of the factors of crisis professionalization, unpreparedness of youth to adapt in spontaneous and quickly changing conditions of the labor market and absence of the conceptual sociological developments in this sphere.

The materials of the research and the offered cognitive scheme are aimed at formation of the methodological basis of studying the problem of managing the professional mobility of the Russian youth for the purpose of overcoming the crisis trajectory of professionalization of the Russian youth and increasing the effectiveness of its professional mobility in the conditions of increased mobility of the whole modern world.

In an increasingly volatile and complex market environment, adaptability becomes essential to success. Basic to every manufacturing strategy is the identification of the technological means of meeting the firm’s competitive priorities. But what should guide the decision‐making process regarding which capabilities to develop, given rapid change and uncertain outcomes? Current manufacturing strategy models fall short of explaining how firms adapt to technological change and what is the source of that change. To meet this need, we can turn to evolutionary economics. The model presented in this paper holds that technologically embodied trade‐offs change the practices of technology users, creating momentum along existent trajectories. The paper reports evidence to support the model, gathered through a series of interviews with technology users, suppliers, and service providers. The findings indicate that manufacturing strategy forms a critical link not only in firms’ ability to adapt to change in their environments, but also in their ability to knowingly shape their futures.

Empirical studies show substantial variation across immigrants in the rate and direction of assimilation along various dimensions (e.g., cross-ethnic contact, language, identity). To explain this variation, past research has focused on identifying exogenous factors, such as discrimination, human capital, and settlement intention. In this chapter we argue that variation in immigrant outcomes emerges endogenously through positive interaction effects between dimensions of assimilation. We propose a new assimilation model in which processes of social influence and selection into congruent social environments give rise to multiple long-term equilibria. In this model, migrants who are already assimilated along many dimensions tend to also adapt along other dimensions, while less assimilated migrants become more strongly embedded in their ethnic group.

To test the assimilation model, we derive a number of hypotheses, which we evaluate using trend analysis and dynamic panel regression on data from the Longitudinal Survey of Immigrants to Canada.

The data mostly confirm the hypotheses, providing overall support for the assimilation model.

Our theory and findings suggest that immigrants would follow divergent assimilation trajectories even in the absence of a priori population heterogeneity in external factors.

The positive interaction effects between cultural and structural dimensions of assimilation suggest that mixed policies that promote integration while seeking to prevent loss of identity go against the natural tendency for cultural and structural assimilation to go hand in hand.

The present chapter proposes a novel model of immigrant assimilation and an empirical test.