Global Grounding in Flexible Robot Tasks With Object Number Variety

Abstract

Automation in flexible environments must account for ambiguities and uncertainties. For example, the number of available objects may vary between different workspaces. To address this, we introduce flexible robot tasks with varieties that incorporate these ambiguities. This approach allows the programmer to estimate the number of required objects without precise knowledge of the world state during execution. With this, we deliberately leverage ambiguities, enabling task reuse across different world states. When executing a task with varieties, physical objects must be mapped to ambiguous object specifications called grounding. This grounding should be globally correct for the entire task and the world state. Rather than establishing a single grounding with fixed object numbers, we examine all the possible object numbers suitable for the task. Exhaustively testing every possibility would require exponential runtime. We overcome this challenge by contributing a global grounding algorithm for tasks with object number variety. Our algorithm uses the Kuhn-Munkres algorithm to establish fixed groundings and efficiently explores the problem space for flexible groundings – achieving a polynomial runtime. Through further optimization with binary search, our prototype demonstrates fast groundings (up to 252 objects in less than one second).