RI: Medium: Computational Models, Interaction Mechanisms, and Planning Algorithms for Semi-Autonomous Systems

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Autonomous systems offer transformational impacts on society as they help reduce human labor, decrease risks and costs, and improve productivity and efficiency. They have been deployed in a wide range of domains from household products to space exploration vehicles. In many areas, however, there are still considerable barriers to the deployment of fully autonomous systems. These barriers range from technological to ethical and legal issues. Examples include driving a car, robot deployment in search and rescue operations, automated farming, and robotic surgery. When full autonomy is not feasible, it is often desirable to automate parts of the entire process. This project offers a comprehensive study of planning for semi-autonomous systems -- systems that are capable of autonomous operation under some conditions, but may require manual control in order to complete the task at hand. Planning for semi-autonomous systems is challenging because it must account for the different skills of the human operator and the automated system, the communication between them required to facilitate smooth transfer of control, the uncertainty about human responsiveness, engagement level and readiness to take over control, and the possibility of human error in interpreting or following the plan. The project takes an interdisciplinary approach that addresses the computational challenges together with the challenges that rise whenever the human is in the loop. With a focus on semi-autonomous driving as the primary domain, research activities include: designing general-purpose graphical models to represent the problem of collaborative control of semi-autonomous systems; developing effective methods to represent and earn competence models of the actors; developing efficient decision-theoretic planning algorithms that exploit heuristic search and reachability analysis to create the shared plan; developing algorithms to compute vital statistics and runtime feedback about the shared plan; developing ways to capture models of situation awareness and human errors, and factor them into the planning process; and creating a set of challenging scenarios and test problems for planning in semi-autonomous systems. Evaluation of the approach is conducted using several testbeds including two realistic driving simulators.