Cooperation of networked multi robot systems using control theory and machine learning

自动驾驶车辆使用专门的传感、感知、通信和驱动套件进行交互，这些套件旨在增强它们在非常具有挑战性的环境中的导航能力。如今，自主车辆、机器人和智能体的使用正在扩展到自动驾驶、军事任务、农业和生产线等不同领域，在这些领域，需要与其他机器人进行协作和互动，以帮助人类。然而，目前用于交互的方法要么基于控制论概念，要么基于统计机器学习，这使得与人类的关系变得复杂，因为人类不能通过概率计算来感知情况。尽管这些机器人可以探索非结构化环境，定位自己，并绘制周围环境的地图，但它们仍然缺乏一定程度的可预测性，源于人类对因果关系的理解。不幸的是，这项研究在机器人学方面并不是很先进，特别是在考虑多个机器人的情况下。因此，在生产线之外以及在家庭、学校和医院等非结构化环境中使用机器人仍然很少见，因为机器人无法被人类轻松理解，这限制了它们的可用性。要进行这样的活动，机器人仅通过使用显示器或语音合成器与人类交流是不够的，但人类必须直观地了解机器人做什么以及为什么。这需要构建类似于人类使用的因果模型。在军事等其他应用中，这些特征也很重要，因为人类必须能够相信，自动车辆和机器人做出的决定遵循因果原理。尽管它令人印象深刻，但似乎不太可能利用统计机器学习方面的进步来实现这一点。这项研究计划旨在开发基于因果推理系统和机器学习的新技术，以有效控制自动车辆和机器人的网络。这些技术将利用车辆建立的通信网络，但将基于对数据和交互中的因果结构的识别。该研究项目还考虑使用现代机器学习体系结构，不仅基于数据，而且基于人类可以理解的模型。将产生新的理论结果，考虑到从人-机器人交互到军事应用的一系列应用，并与业界密切研究合作。对社会互动和困境的分析将被考虑，以及经典环境，包括多辆追逐他人的车辆和儿童玩的游戏，如夺旗。在接下来的五年里，共有六名研究生和五名本科生将在这笔赠款的支持下接受培训，为加拿大的高素质人才做出贡献。

Autonomous vehicles interact among themselves using specialized sensing, perception, communication, and actuation suites tailored to enhance their navigation capabilities in very challenging environments. Nowadays, the uses of autonomous vehicles, robots, and agents is expanding to diverse areas such as self-driving, military missions, agriculture, and production lines, where collaborative activities and interactions with other robots in assisting humans is necessary. However, current methods used for interactions are based on either control theory concepts or statistical machine learning, making the relationship with humans complicated as humans do not perceive situations through probabilistic calculations. Even though these robots can explore unstructured environments, locate themselves, and map their surroundings, they still lack a degree of predictability derived from human understanding of causality. Unfortunately, this investigation is not very advanced in robotics, especially when considering multiple robots. Therefore, the use of robots outside of manufacturing lines and in unstructured settings like homes, schools, and hospitals is still rare, for the inability of robot to be easily understood by humans limit their usability. To perform such activities, it is not sufficient for robots to communicate with humans by using displays or voice synthesizers, but it is necessary that humans intuitively understand what robots do and why. This requires the construction of causal models that are like those used by humans. In other applications, such as the military, these characteristics are also important, for humans must be able to trust that the decisions made by autonomous vehicles and robots follow a cause-and-effect rationale. Impressive as it is, it seems to be very unlikely that this can be achieved using the advances made in statistical machine learning. This research program aims at developing novel techniques based on causal inference systems and machine learning for efficient control of networks of autonomous vehicles and robots. These techniques will take advantage of the communication network established by the vehicles but will be based on the identification of causal structures in the data and in the interactions. The research program also contemplates the use of modern machine learning architectures not only based on data, but also on models that can be understood by humans. New theoretical results will be generated with a range of applications in mind, from human-robot interactions to military applications, with close research collaboration with industry. Analyses of social interactions and dilemmas will be considered as well as classical environments including multiple vehicles pursuing others and games played by children such as capture the flag. A total of six graduate and five undergraduate students will be trained with the support of this grant over the next five years, contributing to Canada's highly qualified personnel.