Digitalization and work life: How new technologies are changing task content and skill demand for five selected occupations.

OBJECTIVE OF THE STUDY: The objective of this study is to understand how digitalization and new technologies are changing task content and skill demand for five selected occupations: business managers, technology innovators, higher education teachers, healthcare professionals and cybersecurity experts. The aim is to study how the productivity of the work of these occupations can be increased by realizing the benefits offered by digitalization. Consequently, this study examines the future division of work between humans and computers and provides recommendations on the required skills and changes in the nature of work that will be increasingly in demand in the near future due to the changes induced by digitalization. METHODOLOGY AND THEORETICAL FRAMEWORK: In this study a theoretical framework based on the bottlenecks to computerization was used to predict future skill demand for the occupations under study. The research was conducted using qualitative multiple case study approach in which each occupation represents one case. The data for the study was collected using semi-constructed interviews with representatives of each of the occupations. There were altogether 26 interviews which were analyzed using theoretical propositions and cross-case comparisons between the different occupations. FINDINGS AND CONCLUSIONS: The main findings of this study indicate that despite of technological advancements, in the occupations under study humans still have a comparative advantage over computers in skills that require analytical and critical thinking, creative intelligence and social and emotional intelligence. Moreover, the common opportunities and challenges of digitalization among the occupations were identified and divided into three following main areas: information efficiency, technology efficiency and people efficiency. The benefits of digitalization can only be realized by tackling the identified challenges that prevent the increase in the efficiency of work for the occupations under study. The role of digitalization in each of the occupations differed depending on how digitalization has changed the efficiency of work and nature of work. For cybersecurity experts, who are diginatives, the changes in both work efficiency and nature of work have and will be constantly increasing. On the other hand, higher education teachers and healthcare professionals are emerging digitalists on the verge of digital transformation, as the needed changes in the nature of work have not yet occurred to increase the efficiency of work accordingly. For business managers, who are efficient digitalists, the increase in the efficiency of work has been significant. However, the changes in the nature of work have been relatively small. Lastly, for technology innovators the changes in the nature of work have been tremendous while the change in work efficiency has not yet been realized. Therefore, they are named as being digital reinventionists. In order to make the work more productive for these five occupations, it is necessary to have the right skills in place and change the nature of work to fit to the needs of the new digital economy

Snake and Snake Robot Locomotion in Complex, 3-D Terrain

Snakes are able to traverse almost all types of environments by bending their elongate bodies in three dimensions to interact with the terrain. Similarly, a snake robot is a promising platform to perform critical tasks in various environments. Understanding how 3-D body bending effectively interacts with the terrain for propulsion and stability can not only inform how snakes move through natural environments, but also inspire snake robots to achieve similar performance to facilitate humans. How snakes and snake robots move on flat surfaces has been understood relatively well in previous studies. However, such ideal terrain is rare in natural environments and little was understood about how to generate propulsion and maintain stability when large height variations occur, except for some qualitative descriptions of arboreal snake locomotion and a few robots using geometric planning. To bridge this knowledge gap, in this dissertation research we integrated animal experiments and robotic studies in three representative environments: a large smooth step, an uneven arena of blocks of large height variation, and large bumps. We discovered that vertical body bending induces stability challenges but can generate large propulsion. When traversing a large smooth step, a snake robot is challenged by roll instability that increases with larger vertical body bending because of a higher center of mass. The instability can be reduced by body compliance that statistically increases surface contact. Despite the stability challenge, vertical body bending can potentially allow snakes to push against terrain for propulsion similar to lateral body bending, as demonstrated by corn snakes traversing an uneven arena. This ability to generate large propulsion was confirmed on a robot if body-terrain contact is well maintained. Contact feedback control can help the strategy accommodate perturbations such as novel terrain geometry or excessive external forces by helping the body regain lost contact. Our findings provide insights into how snakes and snake robots can use vertical body bending for efficient and versatile traversal of the three-dimensional world while maintaining stability

Physics-based Machine Learning Methods for Control and Sensing in Fish-like Robots

Underwater robots are important for the construction and maintenance of underwater infrastructure, underwater resource extraction, and defense. However, they currently fall far behind biological swimmers such as fish in agility, efficiency, and sensing capabilities. As a result, mimicking the capabilities of biological swimmers has become an area of significant research interest. In this work, we focus specifically on improving the control and sensing capabilities of fish-like robots. Our control work focuses on using the Chaplygin sleigh, a two-dimensional nonholonomic system which has been used to model fish-like swimming, as part of a curriculum to train a reinforcement learning agent to control a fish-like robot to track a prescribed path. The agent is first trained on the Chaplygin sleigh model, which is not an accurate model of the swimming robot but crucially has similar physics; having learned these physics, the agent is then trained on a simulated swimming robot, resulting in faster convergence compared to only training on the simulated swimming robot. Our sensing work separately considers using kinematic data (proprioceptive sensing) and using surface pressure sensors. The effect of a swimming body\u27s internal dynamics on proprioceptive sensing is investigated by collecting time series of kinematic data of both a flexible and rigid body in a water tunnel behind a moving obstacle performing different motions, and using machine learning to classify the motion of the upstream obstacle. This revealed that the flexible body could more effectively classify the motion of the obstacle, even if only one if its internal states is used. We also consider the problem of using time series data from a `lateral line\u27 of pressure sensors on a fish-like body to estimate the position of an upstream obstacle. Feature extraction from the pressure data is attempted with a state-of-the-art convolutional neural network (CNN), and this is compared with using the dominant modes of a Koopman operator constructed on the data as features. It is found that both sets of features achieve similar estimation performance using a dense neural network to perform the estimation. This highlights the potential of the Koopman modes as an interpretable alternative to CNNs for high-dimensional time series. This problem is also extended to inferring the time evolution of the flow field surrounding the body using the same surface measurements, which is performed by first estimating the dominant Koopman modes of the surrounding flow, and using those modes to perform a flow reconstruction. This strategy of mapping from surface to field modes is more interpretable than directly constructing a mapping of unsteady fluid states, and is found to be effective at reconstructing the flow. The sensing frameworks developed as a result of this work allow better awareness of obstacles and flow patterns, knowledge which can inform the generation of paths through the fluid that the developed controller can track, contributing to the autonomy of swimming robots in challenging environments

L’implantation de la robotique collaborative et la gestion des ressources humaines dans le secteur manufacturier : soutenir le changement et l’adoption

Ce mémoire de maîtrise explore l’implantation de la robotique collaborative en entreprise sous l’angle des pratiques de gestion et des facteurs humains. La visée initiale de ce projet de recherche visait préalablement à circonscrire l’apport que peut prendre la gestion des ressources humaines (GRH) lors de ce type d’implantation technologique, qui implique une collaboration humain-machine plus accrue qu’auparavant. Initialement, l’objectif était donc d’identifier les pratiques de GRH à mettre en place lors de l’implantation de robots collaboratifs. Cela dit, comme ce projet de recherche présente une démarche exploratoire semi-inductive, l’objectif de recherche a évolué vers plusieurs objectifs. Cette ouverture sur de nouveaux objectifs est subséquente aux résultats obtenus lors de la revue systématique de la littérature et de la collecte de données afin de dresser un portrait plus juste, adapté à l’état des connaissances et au terrain. Les objectifs poursuivis sont les suivants : 1) identifier les pratiques de GRH et d’autres pratiques organisationnelles en matière de gestion du changement facilitant l’implantation et l’adoption des robots collaboratifs 2) identifier les facteurs associés à l’humain, au robot et à l’environnement qui influencent l’implantation des robots collaboratifs, l’adoption et la collaboration entre l’opérateur et le robot

Fabricate

Bringing together pioneers in design and making within architecture, construction, engineering, manufacturing, materials technology and computation, Fabricate is a triennial international conference, now in its third year (ICD, University of Stuttgart, April 2017). Each year it produces a supporting publication, to date the only one of its kind specialising in Digital Fabrication. The 2017 edition features 32 illustrated articles on built projects and works in progress from academia and practice, including contributions from leading practices such as Foster + Partners, Zaha Hadid Architects, Arup, and Ron Arad, and from world-renowned institutions including ICD Stuttgart, Harvard, Yale, MIT, Princeton University, The Bartlett School of Architecture (UCL) and the Architectural Association

Fabricate 2017

Bringing together pioneers in design and making within architecture, construction, engineering, manufacturing, materials technology and computation, Fabricate is a triennial international conference, now in its third year (ICD, University of Stuttgart, April 2017). Each year it produces a supporting publication, to date the only one of its kind specialising in Digital Fabrication. The 2017 edition features 32 illustrated articles on built projects and works in progress from academia and practice, including contributions from leading practices such as Foster + Partners, Zaha Hadid Architects, Arup, and Ron Arad, and from world-renowned institutions including ICD Stuttgart, Harvard, Yale, MIT, Princeton University, The Bartlett School of Architecture (UCL) and the Architectural Association