HiBT: From Hierarchical Concepts to Behavior Trees for Reliable Robot Task Planning

Long-horizon robotic assembly requires planners to preserve object relations, tool states, and ordered preconditions while producing executable control structures. Direct language-model generation leaves these dependencies implicit in a flat token sequence. We propose HiBT, a learned–symbolic planner that formulates Behavior Tree (BT) synthesis as hierarchical concept planning. A learned module predicts a task-conditioned hierarchical interface from the goal and symbolic state; deterministic realization then closes domain dependencies and compiles the grounded plan into a BT. On a 60-instance assembly benchmark with four open-source backbones, HiBT solves all 60 tasks with logically coherent BTs and subsecond planning latency. Matched controls and interface ablations isolate the gains from hierarchical organization and grounding. In LIBERO-Long, HiBT reaches 89.2% final-goal success with shorter rollouts. The dependency-intensive LIBERO-Hier suite further connects these gains to compliance with execution-order and process constraints. A dependency-aware learned–symbolic interface thus improves executable BT synthesis and embodied long-horizon planning.