  Behavior Tree (BT) planning is crucial for autonomous robot behavior control,
yet its application in complex scenarios is hampered by long planning times.
Pruning and heuristics are common techniques to accelerate planning, but it is
difficult to design general pruning strategies and heuristic functions for BT
planning problems. This paper proposes improving BT planning efficiency for
everyday service robots leveraging commonsense reasoning provided by Large
Language Models (LLMs), leading to model-free pre-planning action space pruning
and heuristic generation. This approach takes advantage of the modularity and
interpretability of BT nodes, represented by predicate logic, to enable LLMs to
predict the task-relevant action predicates and objects, and even the optimal
path, without an explicit action model. We propose the Heuristic Optimal
Behavior Tree Expansion Algorithm (HOBTEA) with two heuristic variants and
provide a formal comparison and discussion of their efficiency and optimality.
We introduce a learnable and transferable commonsense library to enhance the
LLM's reasoning performance without fine-tuning. The action space expansion
based on the commonsense library can further increase the success rate of
planning. Experiments show the theoretical bounds of commonsense pruning and
heuristic, and demonstrate the actual performance of LLM learning and reasoning
with the commonsense library. Results in four datasets showcase the practical
effectiveness of our approach in everyday service robot applications.
