Principled Robotic Decision Making via Reachability

** 许多自主系统，如无人机和自动汽车，有可能在我们生活的许多方面产生巨大的积极影响。然而，目前的机器人系统往往在受控环境中运行，通常在没有其他代理的情况下。为了实现它们的潜力，机器人系统仍然需要变得更安全，这样它们就不会伤害人类和其他机器人，并且更聪明，这样它们和人类就可以实现更好的相互理解。这可以通过更有原则的机器人决策算法来实现，这些算法既考虑了领域知识，又考虑了现实世界的数据。为了使机器人更安全，我们使用可达性分析，这是一种安全验证方法，涉及基于机器人系统的动态或可能行为计算可达集。可达集合量化机器人系统的状态或配置的集合，从该集合可以达到目标状态集合。该目标状态集合可以表示期望的目标状态集合，或者不期望的危险状态集合。可达性分析以前已成功地应用于小规模的机器人系统与一般的非线性动力学，在过程噪声，干扰和其他代理的影响下的环境。由于可达性分析考虑了机器人系统的关键先验属性，如动力学和控制动作约束，因此它提供了一个原则性的决策框架，我的研究计划旨在推进这一框架。为了让机器人更聪明，机器学习已经得到了广泛的应用;它的数据驱动特性有可能让机器人感知环境并执行复杂的任务。然而，目前机器人系统需要大量的数据来学习执行任务，并且所学习的行为在不同的环境和任务之间的转移很差。可达性分析有可能通过将机器人系统的关键属性总结为与机器学习算法兼容的形式来帮助克服这些挑战。相反，机器学习也有可能通过推断人类意图来解决安全验证中的挑战。通过这种方式，可达性分析是连接传统机器人决策原则和现代数据驱动技术的桥梁。我的研究项目旨在开发基于可达性分析的实用，有原则的机器人决策算法。在高层次上，目标有两个方面：*1。通过解决可达性分析中的计算挑战，使机器人更安全，并通过与现实感知系统的结合使可达性分析更加实用 **2。通过利用可达性增强机器学习算法，并将机器学习的见解融入人机交互等任务的可达性，使机器人更智能，更易于理解 **

Principled Robotic Decision Making via Reachability******Many autonomous systems, such as unmanned aerial vehicles and autonomous cars, have potential to make great positive impact in many aspects of our lives. However, currently robotic systems tend to operate in controlled environments, often in the absence of other agents. To realize their potential, robotic systems still need to become both safer so that they do not harm humans and other robots, and smarter so that they and humans can achieve better mutual understanding. This can be done through more principled robotic decision making algorithms that account for both domain knowledge and real-world data.******To make robots safer, we use reachability analysis, a safety verification method that involves computing the reachable set based on the dynamics, or possible behaviours, of a robotic system. The reachable set quantifies the set of states or configurations of a robotic system from which a target set of states can be reached. This target set of states may represent a set of goal states which are desirable, or a set of dangerous states which are undesirable. Reachability analysis has previously been successfully applied to small scale robotic systems with general nonlinear dynamics, under the influence of process noise, disturbances, and other agents in the environment. Since it accounts for key prior properties of robotic systems such as dynamics and control action constraints, reachability analysis provides a principled decision making framework that my research program aims to advance.******To make robots smarter, machine learning has been used extensively; its data-driven nature has the potential to allow robots to sense their environment and to perform complex tasks. However, currently a large amount of data is needed for a robotic system to learn to perform a task, and the learned behaviour transfers poorly across different environments and tasks. Reachability analysis has the potential to help overcome these challenges by summarizing key properties of robotic systems into a form that is compatible with machine learning algorithms. Conversely, machine learning also has the potential to address challenges in safety verification by inferring human intent. In this way, reachability analysis is a bridge that connects traditional principles of robotic decision making to modern data-driven techniques.******My research program aims to develop practical, principled robotic decision making algorithms based on reachability analysis. At a high level, the objectives are two-fold:******1. To make robots safer by addressing the computational challenges in reachability analysis and making reachability analysis more practical through its incorporation with realistic perception systems******2. To make robots smarter and more understandable by utilizing reachability to augment machine learning algorithms, and by incorporating insights from machine learning into reachability in tasks such as human-robot interactions**