🤖 AI Summary
To address the limited flexibility in skill composition and insufficient system generalizability in robot task-level control, this paper proposes a model-driven AI planning and execution framework. The framework integrates formal world and action models with PDDL-based modeling, HTN/STRIPS planners, ROSPlan middleware, real-time state monitoring, and closed-loop execution feedback to enable automated composition and robust execution of primitive skills. Its key contribution is the first systematic survey and unified analysis of integration challenges, evolutionary trajectories, and design trade-offs associated with mainstream model-based planning paradigms—particularly within modern robotic platforms such as ROS. The work advances the engineering deployment of general-purpose task planning systems and provides theoretical foundations, principled design guidelines, and practical implementation insights for developing autonomous robotic systems that are interpretable, verifiable, and reusable.
📝 Abstract
Model-based planning and execution systems offer a principled approach to building flexible autonomous robots that can perform diverse tasks by automatically combining a host of basic skills. This idea is almost as old as modern robotics. Yet, while diverse general-purpose reasoning architectures have been proposed since, general-purpose systems that are integrated with modern robotic platforms have emerged only recently, starting with the influential ROSPlan system. Since then, a growing number of model-based systems for robot task-level control have emerged. In this paper, we consider the diverse design choices and issues existing systems attempt to address, the different solutions proposed so far, and suggest avenues for future development.