Learning Compositional Symbolic Task Rules from Demonstrations with Inductive Logic Programming

• URL: http://arxiv.org/abs/2605.26828v1
• Date: Tue, 26 May 2026 10:45:29 GMT
• Title: Learning Compositional Symbolic Task Rules from Demonstrations with Inductive Logic Programming
• Abstract summary: We show how to represent and learn robotic tasks with inductive logic programming(ILP)<n>We infer symbolic rules from demonstrations and prior (domain) knowledge, and reuses learned rules when learning higher-level task structure.<n>These results provide preliminary evidence that decomposed ILP is a feasible approach to task-level LfD.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Learning from Demonstration~(LfD) should capture not only how a task is executed, but also its high-level task structure that explains the demonstrated behavior. As robots become more autonomous, such task representations must be inspectable, reusable, and human-interpretable. To address this, we study how to represent and learn robotic tasks with inductive logic programming~(ILP) by decomposing a complex task into a series of simpler learning objectives at different abstraction (ontological) levels. The system infers symbolic rules from demonstrations and prior (domain) knowledge, and reuses learned rules when learning higher-level task structure. We evaluate the approach in a synthetic block-assembly scenario and show that the learned abstractions are interpretable and support strong generalization to harder, held-out tasks with unseen objects. These results provide preliminary evidence that decomposed ILP is a feasible approach to task-level LfD.

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