I Won't Know Until I Try: Interactive Information Gathering for Long-Horizon Manipulation

Abstract

Long-horizon manipulation often depends on object properties that cannot be reliably inferred from appearance alone. Physical interaction can reveal these properties, but it can also change the world in ways that invalidate subsequent actions. We present a task and motion planning (TAMP) framework that integrates task-directed interactive information-gathering with interleaved planning and execution. Building on PDDLStream, our framework plans optimistically with these interactive actions, verifies their outcomes during execution, and adapts the remaining plan by retaining still-applicable actions and replanning only the connections needed to continue execution. We evaluate the framework in simulation and on a real robot across tasks that require determining whether an object is movable, a pen has ink, a drawer can be opened, or a container is empty. These experiments demonstrate how interaction-driven state updates and recovery replanning support continued execution under uncertain outcomes and external disturbances.