Learning and Adaptation for Long-Term Autonomous Robotics Applications

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The majority of today's robots are unable to reliably operate in unknown, changing and generally uncontrolled environments. Their systems rely on knowing in advance the specifics of every possible situation they might encounter. They are not able to adapt to new situations. This is one of the main reasons why, to date, autonomous (mobile) robots have been primarily deployed in highly controlled environments (e.g. manufacturing plants or warehouses). To overcome this major limitation, the proposed research program will develop novel robotics algorithms and systems that extend robot operations to uncontrolled environments by enabling robots to learn and adapt. Such capabilities will lead to a major paradigm shift in robotics towards the long-term deployment of autonomous robots in uncontrolled real-world application scenarios such as inspection, remote sensing, resources monitoring, and space exploration.**Assuming that useful a priori information about a robotic system and its environments is available, we focus on hybrid model-and-data-driven approaches, which aim to combine the best of two worlds: a priori model information assures safe operation initially (e.g. based on a robust model-based controller design), while based on the data collected during operation the model can be refined and the robot's performance can be gradually improved. **This approach blends the worlds of machine learning and data mining with classical control and estimation theory, and has proven to be successful in isolated scenarios. Recent work by the applicant has shown that difficult-to-model aerodynamic effects can be learned effectively and efficiently resulting in precise high-speed flight maneuvers. She has also shown that a ground robot can learn to compensate for unknown rough terrain. These results show that hybrid model-and-data-driven control is a promising new direction for robotics that has the potential to help bridge the gap from simple lab experiments to serious long-term service applications. **We seek to enable reliable