DeepField- Deep Learning in Field Robotics: from conceptualization towards implementation

INESC TEC wants to explore research in deep learning for field robotics applications following the trend in Europe Industry 4.0. For addressing these challenges, core competencies in deep-learning robotics applications need to be acquired and develop from a new scientific and technological perspective. Robotics research areas of interest such as: autonomous underwater intervention, underwater scene understanding, semantic mapping, multi-robot cooperation and aerial mapping, can benefit tremendously from deep-learning research by bringing a new approach and fresh perspective on how to solve some of the problems in this type of robotic applications. Robots are active agents that need to interact with the physical world, to do so, robots are equipped with different sensors, whose data is used to build models that ultimately will allow robots to plan actions and make decisions. Currently, there is strong focus in developing deep learning strategies “data driven” to help solve this perception problem, even though these approaches work well in dataset and benchmark scenarios. There are still strong limitations in the use of this techniques in real world robot activities, specially due to the strong dynamics in robots operational environment, that is pushing the development of new tools and methods to make these approaches feasible in the real world. INESC TEC is strongly committed to become a centre of excellence with focus on field robotics, in particular, in the aerial and underwater robotics domain. In the last years, the centre for Robotics and Autonomous systems, of INESC TEC has advance its scientific knowledge in sensing and perception methods for robots navigation and localization in harsh operational environments. The key objective of INESC TEC is to become one of the European references in field robotics, and help to bring robot technology to solve real life problems where human intervention is still limited or non-existent. The proposal aims at creating solid knowledge and productive links in the global field of deep learning in field robotics between INESC TEC and established leading research European institutions, capable of enhancing the scientific and technological capacity of INESC TEC and linked institutions (as well as the capacity of partnering institutions involved in the twinning action), helping raising its staff’s research profile and its recognition as an European research centre of excellence in field robotics. In particular, it takes INESC TEC and places it as the pivot of a network of excellence, involving four international leaders in deep learning technology and fied robotics.