In various industry sectors all over the world, the ongoing digital transformation helps to unlock benefits for individual components, involved processes, stakeholders as well as the overarching system (e.g., the national economy).In this context, the rail transport sector can particularly benefit from the increased prevalence of sensor systems and the thereby increased availability of related data.As rail transport, by nature, is an integrated transport mode that contains both freight and passenger transport within the same transport network, benefits achieved for the service quality of freight transport also lead to improvements for passenger transport (e.g., punctuality or uptime of rolling stock).This technical paper presents a method to monitor the condition of the existing rail infrastructure as well as the rolling stock by obtaining insights from raw sensor data (e.g., locations and acceleration data).The data is collected with telemetry-units (i.e.multiple sensors integrated with a telematics device to enable data transmission) mounted on a fleet of on-rail freight wagons.In addition, the proposed method is applied to an exemplary set of extracted real-world data.

Start-ups, especially those with a focus on technical products or services, are known to be a key driver of innovation. Although engineering students are increasingly interested in founding and their start-ups are of considerable quality, Germany has a small number of new start-ups, as well as an equally low rate of innovation in the knowledge-based and high-technology sector compared to some other leading nations. Engineering students, which are most likely to found a tech related start-up seem to lack knowledge and incentives to start an autonomous venture. A survey of 109 engineering students, targeted this issue. It showed that only that 56,7% of the surveyed students could imagine establishing a start-up after completing their degree, but only 6% of the students actually plan to start a business, which was mostly due to the lack of preparation and the individual fear of failure. To face these challenges, the seminar “How to become an entrepreneur” is developed within the project ELLI 2. The extracurricular seminar provides students with skills they need to generate ideas, setting up a business plan and launch their business. Its program follows a two-pronged teaching approach. Theoretical frameworks as well as hands-on problem solving are integral parts of the learning experience to equip the students not only with knowledge, but with the ability of applying the acquired information. This paper outlines the approach of the developed seminar which is preparing students in an academic setting for applying their entrepreneurial knowledge.

Guided Policy Search enables robots to learn control policies for complex manipulation tasks efficiently. Therein, the control policies are represented as high-dimensional neural networks which derive robot actions based on states. However, due to the small number of real-world trajectory samples in Guided Policy Search, the resulting neural networks are only robust in the neighbourhood of the trajectory distribution explored by real-world interactions. In this paper, we present a new policy representation called Generative Motor Reflexes, which is able to generate robust actions over a broader state space compared to previous methods. In contrast to prior state-action policies, Generative Motor Reflexes map states to parameters for a state-dependent motor reflex, which is then used to derive actions. Robustness is achieved by generating similar motor reflexes for many states. We evaluate the presented method in simulated and real-world manipulation tasks, including contact-rich peg-in-hole tasks. Using these evaluation tasks, we show that policies represented as Generative Motor Reflexes lead to robust manipulation skills also outside the explored trajectory distribution with less training needs compared to previous methods.