RE:BT-Espresso: Improving Interpretability and Expressivity of Behavior Trees Learned from Robot Demonstrations

Abstract

Behavior trees (BTs) are hierarchical agent control architectures popular for robot task-level planning that can be autonomously learned from robot demonstrations via decision tree (DT) intermediaries, making them accessible to non-expert users. Conversion algorithms from DTs to BTs, such as the BT-Espresso algorithm, focus on replicating DT logic in a BT format but do not exploit the strengths of the BT architecture. We introduce the Representation Exploitation of BT-Espresso (RE:BT-Espresso) algorithm, which builds on BT-Espresso and improves the learned BT's interpretability and expressivity. RE:BT-Espresso improves interpretability by removing logical redundancies in the generated BTs and improves expressivity by exploiting desired BT structures, such as adding Inverter nodes, Repeater sequences, and Parallel Selector Action nodes that gives the user a choice of actions for state spaces that did not resolve to a concise action in the DT. The RE:BT-Espresso algorithm was evaluated against BT-Espresso using demonstration data synthesized by BTs. When compared to the synthesized BTs using graph edit distance (GED), RE:BT-Espresso outscored BT-Espresso on 54 subtrees, tied on 178, and lost on 2. Further, the proposed reduction strategies reduced the number of nodes in a generated tree by a median of 7.82%. The results validate improved interpretability and expressivity of learned RE:BT-Espresso task-level BT policies from robot demonstration.