BTPG: A Platform and Benchmark for Behavior Tree Planning in Everyday Service Robots

Xinglin Chen, Yishuai Cai, Minglong Li, Yunxin Mao, Zhou Yang, Wenjing Yang, Weixia Xu, Ji Wang

Abstract: Behavior Trees (BTs) are a widely used control architecture in robotics, renowned for their robustness and safety, which are especially crucial for everyday service robots. Recently, several methods have been proposed to automatically plan BTs to accomplish specific tasks. However, existing research in BT planning lacks two main aspects: (1) the absence of a standard platform for modeling and planning BTs, along with testing benchmarks; and (2) insufficient metrics for a comprehensive evaluation of BT planning algorithms. In this paper, we propose Behavior Tree Planning Gym (BTPG), the first platform and benchmark for BT planning in everyday service robots. In BTPG, behavior nodes are represented by predicate logic, and objects are categorized to better define the predicate domains and action models. The BT planning problem is then formulated in the STRIPS style. We support four environments and three simulators with different action models, which cover most of the needs of everyday service activities. We design a dataset generator for each environment and test three state-of-the-art BT planning algorithms, as well as one proposed by us, using various common metrics. In addition, we design three advanced metrics, planning progress, region distance, and execution robustness, to gain deeper insights into these BT planning algorithms. With a standard test benchmark, we hope BTPG can inspire and accelerate progress in the field of BT planning. Our codes are available at https://github.com/DIDS-EI/BTPG.

External IDs:dblp:conf/ijcai/ChenCLMY0XW25