DEPT-BT: Learning Parameterized and Revisable Behavior Trees From a Single Demonstration

Abstract

Learning from Demonstration (LfD) combined with Behavior Trees (BTs) aims to lower the programming burden required to construct robot task programs from demonstrations. However, existing approaches have two structural limitations: skills are bound to specific object instances with no mechanism for runtime rebinding, and inferred symbolic conditions are compiled as frozen constraints that cannot be independently verified or revised. This letter presents DEPT-BT, a framework that transforms single demonstrations into parameterized, executable, and maintainable behavior tree programs. DEPT-BT introduces anchor-based parameterized skill operators that decouple skills from demonstration objects, enabling task-level generalization through runtime parameter rebinding. The framework also incorporates a verify-revise closed-loop mechanism that maintains symbolic conditions as editable entities, supporting targeted failure attribution and correction without complete re-demonstration. Experiments on four manipulation tasks yield an overall success rate of approximately 90%. DEPT-BT attains 95% success when transferring skills to novel objects, compared to 0% for the baseline, and the revision mechanism reduces failure recovery time by 64% compared to full re-demonstration.