Principled Robotic Decision Making via Reachability

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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**