Inference of Human-derived Specifications of Object Placement via Demonstration

Alex Cuellar, Ho Chit Siu, Julie Shah

Keywords: HAI: Cognitive Modeling, ROB: Human-Robot Interaction

TL;DR: This paper introduces PARCC, a logical specification langauge over object relations and an algorithm to infer specifications from demonstrations.

Abstract: As robots' manipulation capabilities improve for pick-and-place tasks (e.g., object packing, sorting, and kitting), methods focused on understanding human-acceptable object configurations remain limited expressively with regard to capturing spatial relationships important to humans. To advance robotic understanding of human rules for object arrangement, we introduce positionally-augmented RCC (PARCC), a formal logic framework based on region connection calculus (RCC) for describing the relative position of objects in space. Additionally, we introduce an inference algorithm for learning PARCC specifications via demonstrations. Finally, we present the results from a human study, which demonstrate our framework's ability to capture a human's intended specification and the benefits of learning from demonstration approaches over human-provided specifications.

Paper Type: Previously Published Paper

Venue For Previously Published Paper: IJCAI25

Submission Number: 6