Deep Drone Racing: From Simulation to Reality with Domain Randomization

Dynamically changing environments, unreliable state estimation, and operation under severe resource constraints are fundamental challenges that limit the deployment of small autonomous drones. We address these challenges in the context of autonomous, vision-based drone racing in dynamic environments. A racing drone must traverse a track with possibly moving gates at high speed. We enable this functionality by combining the performance of a state-of-the-art planning and control system with the perceptual awareness of a convolutional neural network (CNN). The resulting modular system is both platform- and domain-independent: it is trained in simulation and deployed on a physical quadrotor without any fine-tuning. The abundance of simulated data, generated via domain randomization, makes our system robust to changes of illumination and gate appearance. To the best of our knowledge, our approach is the first to demonstrate zero-shot sim-to-real transfer on the task of agile drone flight. We extensively test the precision and robustness of our system, both in simulation and on a physical platform, and show significant improvements over the state of the art.Comment: Accepted as a Regular Paper to the IEEE Transactions on Robotics Journal. arXiv admin note: substantial text overlap with arXiv:1806.0854

Initial Career and Work Meanings in Seven European Countries

The study explores the initial careers of two target groups of youngsters in seven European countries. Career patterns are constructed through cluster analysis on data gathered from the youngsters through a retrospective self-report procedure covering a period of 2;5 years. Six career patterns are identified across countries by the activities taking place (employment, various educational preparations, unemployment, military or civil service) and further described by personal and work-related variables as well as by work meanings and psychological well-being in the second year on the labor market. Some suggestions are given for further research and implications for career counseling, career education, organizational career guidance and development

Understanding the Education Trajectories of Young Black Men in New York City: Elementary and Middle-School Years

Making targeted decisions about how, when, and where to intervene to improve educational outcomes for black males requires understanding the complex pathways that shape these outcomes. This study, undertaken for the Black Male Donor Collaborative, uses longitudinal data on a cohort of black males from New York City Schools to gain insights about the different possible student paths, with specific focuses on middle school and math scale scores