3 accepted papers
State uncertainty is a primary obstacle to effective long-horizon robot task planning. State uncertainty can be decomposed into spatial uncertainty—resolved using SLAM—and uncertainty about the objects in the environment, formalized as the object scouting problem and modeled using the Locally Observ
Real-world robot task planning is intractable in part due to partial observability. A common approach to reducing complexity is introducing additional structure into the decision process, such as mixed-observability, factored states, or temporally-extended actions. We propose the locally observable…
We address the problem of efficiently learning high-level abstractions for task-level robot planning. Existing approaches require large amounts of data and fail to generalize learned abstractions to new environments. To address this, we propose to exploit the independence between spatial and non-s…