Relying upon an original (country-sector-year) measure of robotic capital ($RK$), we investigate the degree of complementarity/substitutability between robots and workers at different skill levels. We employ nonparametric methods to estimate elasticity of substitution patterns between $RK$ and skilled/unskilled labor over the period 1995–2009. We show that: i) on average, $RK$ exhibits less substitutability with skilled workers compared to unskilled workers, indicating a phenomenon of “RK-Skill complementarity”. This pattern holds in a global context characterized by significant heterogeneity; ii) the dynamic of “RK-Skill complementarity” has increased since the early 2000s; iii) the observed strengthening is more prominent in OECD countries, as opposed to non-OECD countries, and in the Manufacturing sector, compared to non-Manufacturing industries.

In recent years, unmanned aerial vehicles (UAVs) have been applied in underground mine inspection and other similar works depending on their versatility and mobility. However, accurate localization of UAVs in perceptually degraded mines is full of challenges due to the harsh light conditions and similar roadway structures. Due to the unique characteristics of the underground mines, this paper proposes a semantic knowledge database-based localization method for UAVs. By minimizing the spatial point-to-edge distance and point-to-plane distance, the relative pose constraint factor between keyframes is designed for UAV continuous pose estimation. To reduce the accumulated localization errors during the long-distance flight in a perceptual-degraded mine, a semantic knowledge database is established by segmenting the intersection point cloud from the prior map of the mine. The topological feature of the current keyframe is detected in real time during the UAV flight. The intersection position constraint factor is constructed by comparing the similarity between the topological feature of the current keyframe and the intersections in the semantic knowledge database. Combining the relative pose constraint factor of LiDAR keyframes and the intersection position constraint factor, the optimization model of the UAV pose factor graph is established to estimate UAV flight pose and eliminate the cumulative error. Two UAV localization experiments conducted on the simulated large-scale Edgar Mine and a mine-like indoor corridor indicate that the proposed UAV localization method can realize accurate localization during long-distance flight in degraded mines.

This paper explores the (de-)routinisation of employment structure in developing countries, through the case of Morocco. We investigate employment (de-)routinisation from an often-overlooked perspective, aiming to elucidate the interplay between the dynamics of occupational employment composition by the level of routine tasks intensity and two structural aspects: premature deindustrialisation and the prevalence of informal labour.

Our findings, based on tertile analysis and regressions, do not fully support the hypothesis of employment structure de-routinisation. At the same time, we could not identify a clear process of routinisation similar to that observed in developing countries undergoing the first stage of the traditional structural transformation process. Rather, we identified an inverted U-shaped pattern in the dynamics of occupational employment, indicative of a rise in intermediate routine-intensive occupations.

We emphasise two key factors, with opposite effects that have contributed to this atypical pattern: The first aspect is premature deindustrialisation, which according to our shift-share decomposition, has adversely affected highly routine-intensive jobs, contrasting with the routinisation trend observed in countries that have experienced a more traditional process of structural transformation. The influence of premature deindustrialisation in terms of de-routinisation is somewhat mitigated by the increasing prevalence of occupations demanding intermediate routine tasks, particularly within the services and construction sector. Regarding the second structural aspect – the prevalence of informal labour – our three-way interaction model indicates a lower susceptibility of informal jobs to de-routinisation compared to their formal counterparts within the same industry. Consequently, the prevalence of informal employment has slowed down the process of de-routinisation of employment structure.

This paper introduces a lower limb exoskeleton for gait rehabilitation, which has been designed to be adjustable to a wide range of patients by incorporating an extension mechanism and series elastic actuators (SEAs). This configuration adapts better to the user’s anatomy and the natural movements of the user’s joints. However, the inclusion of SEAs increases actuator mass and size, while also introducing nonlinearities and changes in the dynamic response of the exoskeletons. To address the challenges related to the human–exoskeleton dynamic interaction, a nonsingular terminal sliding mode control that integrates an adaptive parameter adjustment strategy is proposed, offering a practical solution for trajectory tracking with uncertain exoskeleton dynamics. Simulation results demonstrate the algorithm’s ability to estimate unknown parameters. Experimental tests analyze the performance of the controller against uncertainties and external disturbances.

Motivated by distributional concerns raised by recent breakthroughs in AI and robotics, we ask how workers would prefer to manage an episode of automation in a task-based model, which distinguishes between automation and traditional technical progress. We show that under majority voting with the option to implement a “partial” UBI (as transfers to workers) it is optimal to tax capital at a higher rate than labor in the long run to fund the partial UBI. We show that, unlike traditional technical progress, automation always lowers the labor share in the long run, justifying distributional concerns. A quantitative analysis of an episode of automation for the US economy shows that it is optimal from the workers’ perspective to lower capital taxes and transfers over the transition. Nevertheless, this policy increases worker welfare by only 0.7% in consumption-equivalent terms, compared with a 21.6% welfare gain to entrepreneurs, because the welfare gains to workers from lower capital taxes are second-order, while the gains to entrepreneurs are first-order.

In this study, we present a hybrid kinematic modeling approach for serial robotic manipulators, which offers improved accuracy compared to conventional methods. Our method integrates the geometric properties of the robot with ground truth data, resulting in enhanced modeling precision. The proposed forward kinematic model combines classical kinematic modeling techniques with neural networks trained on accurate ground truth data. This fusion enables us to minimize modeling errors effectively. In order to address the inverse kinematic problem, we utilize the forward hybrid model as feedback within a non-linear optimization process. Unlike previous works, our formulation incorporates the rotational component of the end effector, which is beneficial for applications involving orientation, such as inspection tasks. Furthermore, our inverse kinematic strategy can handle multiple possible solutions. Through our research, we demonstrate the effectiveness of the hybrid models as a high-accuracy kinematic modeling strategy, surpassing the performance of traditional physical models in terms of positioning accuracy.

We study whether the increased adoption of available automation technologies allows economies to avoid the negative effect of aging on per capita output. We develop a quantitative theory in which firms choose to which extent they automate in response to a declining workforce and rising old-age dependency. An important element in our model is the integration of two capital types: automation capital that acts as a substitute to human labor, and traditional capital that is a complement to labor. Empirically, our model's predictions largely match data regarding automation (robotization) density across OECD countries. Simulating the model, we find that aging-induced automation only partially compensates the negative growth effect of aging in the absence of technical progress in automation technology. One reason is that automated tasks are no perfect substitutes for non-automated tasks. A second reason is that automation raises the interest rate and thus inhibits positive behavioral reactions to aging (later retirement and investment in human capital). Moreover, increased automation generates a falling net labor share of income and rising welfare inequality. We evaluate alternative policy responses to cope with this inequality.

Since 2010, the UK government has transformed social security administration using digital technology and automated instruments to create and deliver a single working-age benefit known as Universal Credit (UC). Social policy scholars have given much attention to the key policy tenets of UC but engaged less with leading aspects of automated and digital delivery and their relationship to different forms of administrative burdens for UC recipients. This article addresses this empirical and conceptual gap by drawing on administrative burdens literature to analyse empirical data from forty-four interviews with UC recipients. We conclude by highlighting three costs: temporal, financial, and emotional. These costs illustrate the political dimensions of technical features of UC, as they affect accountability procedures and paths to legal entitlements that have bearings on certain claimants’ rights.

By introducing automation development into a labor search model, this paper obtains that the increasing importance of automation in production may be responsible for the reduction in job reallocation along the transitional dynamics path. In the long run, we find automation also increases the total unemployment rate and reduces overall labor force participation. In addition, decreasing any disparity between differently skilled labor is detrimental to job reallocation along the transitional dynamics path, and both the long-run total unemployment rate and overall labor market participation will fall. Nevertheless, appropriate government subsidy policies can improve business dynamics across the labor market.

Does providing information about the costs and benefits of automation affect the perceived fairness of a firm's decision to automate or support for government policies addressing automation's labor market consequences? To answer these questions, we use data from vignette and conjoint experiments across four advanced economies (Australia, Canada, the UK, and the US). Our results show that despite people's relatively fixed policy preferences, their evaluation of the fairness of automation—and therefore potentially the issue's political salience—is sensitive to information about its trade-offs, especially information about price changes attributable to automated labor. This suggests that the political impact of automation may depend on how it is framed by the media and political actors.

Flexible endoscopy is the gold standard modality for diagnosis and therapeutic intervention of various colorectal conditions. A high bar is currently set for any new technology to replace the current modern colonoscope, but limitations do exist. For a robotic system to gain acceptance, ideally a clear advantage over the established standard needs to be demonstrated. The application of robotic technology inspired by locomotion observed in animals has been demonstrated in many fields including colonoscopy. A myriad of novel concepts has been proposed, which can overcome the anatomical and technical challenges.

This review discusses novel and innovative examples of bioinspired robotic locomotion in the colon with a detailed comparison of studies alongside separating the discussion by animal sections of insect, marine and reptile locomotion. We also discuss the current advantages and challenges a bioinspired robot will bring to the colon.

Bioinspired robotics in the colon is an exciting field of research with the potential to improve upon current existing high standards of practice in colonoscopy. By addressing areas that the conventional colonoscope is weaker in, studies are demonstrating improvement upon current limitations of standard practice and providing an insight into new methods of engineering and fabrication. Focus on the technological, mechanical and regulatory barriers is key to achieve acceptance into standard practice and will allow the aspiration of a safe, low discomfort, low cost and potentially fully autonomous robotic colonoscope to be not too distant in the future of colonoscopy.

Although several studies have revealed that fractional order controllers usually outperform conventional integer-order control solutions, fractional order controllers are not yet widely applied in industrial applications due to their complex mathematical background. In this paper, further improvements of a simple weighted sum feedback design are introduced that imitates the behavior of a fractional order controller but is free from its various formal restrictions. The proposed control solution has the main characteristics of a fractional order controller, such as finite memory length, excellent transient response with no overshoot and robust behavior, but it is placed into a much simpler mathematical framework. In the current paper, a simple derivative term was incorporated in the design which made the controller’s output more stable by completely eliminating output chattering. The proposed control method was developed for a general second-order system. It was tested in a fixed point iteration-based adaptive control scenario, through simulations using a robotic example and on experimental basis as well, utilizing a simple one-degree-of-freedom electromechanical system. The presented experiments are the first systematic investigations of the fixed point iteration-based adaptive control method.

Offshoring and automation are sources of wage polarization. We reassess these two determinants of wage polarization in a single directed technical change setup that encompasses routine and nonroutine production. We empirically establish the conditional positive relationship between automation and relocations on one side and wage polarization on the other. Theoretically, we show that wage polarization increases with automation and offshoring. In particular, wage polarization in favor of domestic (nonroutine) high(low)-skilled workers is positively affected by an increase in domestic (nonroutine) high(low)-skilled labor quantity and/or absolute productivity. Additionally, it is also positively influenced by a rise in foreign (routine) medium-skilled labor quantity and/or absolute productivity while negatively impacted by an increase in domestic (routine) medium-skilled labor quantity and/or absolute productivity. We show that the effect of offshoring on wage polarization diminishes with the degree of substitutability between routine and nonroutine sectors in the economy, with the share of machines in the production of intermediate goods, and with the scale effect. We quantitatively assess the impact through a thorough data-based calibration exercise, where the numerical results confirmed the theoretical findings.

Does the threat of automation of workers’ employment provoke distinct policy preferences from that of globalization? We present hypotheses about how these different threats affect support for policies to prevent such shocks as well as policies to compensate via redistribution. Using vignettes and conjoint experiments embedded in survey evidence from Spain, we find that the threat of automation does not provoke any greater demand for redistribution than does openness. Nor does job loss due to automation provoke beliefs of greater deservingness of compensatory transfers, compared to job loss from openness. While the threat of offshoring and hiring foreign workers increases support for policies to prevent this process from occurring, scenarios of robot substitution do not provoke a similar reaction. These results suggest policies to prioritize automation over openness may gain less political traction.

This article presents a system architecture and a set of interfaces that can build scalable information systems capable of large city modeling based on dynamic geospatial knowledge graphs to avoid pitfalls of Web 2.0 applications while blending artificial and human intelligence during the knowledge enhancement processes. We designed and developed a GeoSpatial Processor, an SQL2SPARQL Transformer, and a geospatial tiles ordering tasks and integrated them into a City Export Agent to visualize and interact with city models on an augmented 3D web client. We designed a Thematic Surface Discovery Agent to automatically upgrade the model’s level of detail to interact with thematic parts of city objects by other agents. We developed a City Information Agent to help retrieve contextual information, provide data concerning city regulations, and work with a City Energy Analyst Agent that automatically estimates the energy demands for city model members. We designed a Distance Agent to track the interactions with the model members on the web, calculate distances between objects of interest, and add new knowledge to the Cities Knowledge Graph. The logical foundations and CityGML-based conceptual schema used to describe cities in terms of the OntoCityGML ontology, together with the system of intelligent autonomous agents based on the J-Park Simulator Agent Framework, make such systems capable of assessing and maintaining ground truths with certainty. This new era of GeoWeb 2.5 systems lowers the risk of deliberate misinformation within geography web systems used for modeling critical infrastructures.