Learning and Adaptation for Long-Term Autonomous Robotics Applications

今天的大多数机器人都不能在未知、变化和通常不受控制的环境中可靠地运行。他们的系统依赖于提前了解他们可能遇到的每一种可能情况的细节。他们不能适应新的情况。这是迄今为止自主(移动)机器人主要部署在高度受控环境(例如制造工厂或仓库)的主要原因之一。为了克服这一主要限制，拟议的研究计划将开发新的机器人算法和系统，通过使机器人能够学习和适应，将机器人的操作扩展到非受控环境。这些能力将导致机器人学的重大范式转变，转向在非受控的真实世界应用场景中长期部署自主机器人，如检查、遥感、资源监测和空间探索。**假设关于机器人系统及其环境的有用的先验信息是可用的，我们专注于混合模型和数据驱动的方法，旨在结合两个世界的最好：先验模型信息确保最初的安全操作(例如基于健壮的基于模型的控制器设计)，而基于操作过程中收集的数据可以改进模型并逐渐提高机器人的性能。**这种方法将机器学习和数据挖掘与经典的控制和估计理论相结合，并已被证明在孤立的情况下是成功的。申请人最近的工作表明，难以建模的空气动力学效应可以有效和高效地学习，从而实现精确的高速飞行动作。她还展示了地面机器人可以学习补偿未知的崎岖地形。这些结果表明，混合模型和数据驱动的控制是机器人学一个很有前途的新方向，有可能帮助弥合从简单的实验室实验到严肃的长期服务应用之间的差距。**我们寻求实现可靠的

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