SpecRLBench: A Benchmark for Generalization in Specification-Guided Reinforcement Learning
2026-04-27 • Machine Learning
Machine Learning
AI summaryⓘ
The authors introduced SpecRLBench, a new test set to see how well reinforcement learning methods can follow rules written in a special logic called linear temporal logic (LTL) across different tasks and environments. Their benchmark covers tasks like navigation and manipulation, including changing settings and different robots, to check how these methods handle new and tricky situations. They tested existing methods and found out what works well and where challenges remain when tasks or environments get more complex. This work helps researchers compare methods fairly and improve how these systems learn to follow complicated rules.
reinforcement learninglinear temporal logicspecification-guided learninggeneralizationnavigation tasksmanipulation tasksdynamic environmentsrobot dynamicsbenchmarkobservation modalities
Authors
Zijian Guo, İlker Işık, H. M. Sabbir Ahmad, Wenchao Li
Abstract
Specification-guided reinforcement learning (RL) provides a principled framework for encoding complex, temporally extended tasks using formal specifications such as linear temporal logic (LTL). While recent methods have shown promising results, their ability to generalize across unseen specifications and diverse environments remains insufficiently understood. In this work, we introduce SpecRLBench, a benchmark designed to evaluate the generalization capabilities of LTL-based specification-guided RL methods. The benchmark spans multiple difficulty levels across navigation and manipulation domains, incorporating both static and dynamic environments, diverse robot dynamics, and varied observation modalities. Through extensive empirical evaluation, we characterize the strengths and limitations of existing approaches and reveal the challenges that emerge as specification and environment complexity increase. SpecRLBench provides a structured platform for systematic comparison and supports the development of more generalizable specification-guided RL methods. Code is available at https://github.com/BU-DEPEND-Lab/SpecRLBench.