Dylan Shell, Texas A&M University
Everyday tasks combine discrete and geometric decision-making. The robotics, AI, and formal methods communities have concurrently explored different planning approaches, producing techniques with different capabilities and trade-offs. We identify the combinatorial and geometric challenges of planning for everyday tasks, develop a hybrid planning algorithm, and implement an extensible planning framework. In ongoing work, we are improving the scalability and extensibility of our task-motion planner and developing planner-independent evaluation metrics.