SpecRLBench: A Benchmark for Generalization in Specification-Guided Reinforcement Learning

2026-04-27

AI Summary

Existing specification-guided reinforcement learning approaches based on Linear Temporal Logic (LTL) lack systematic evaluation of their generalization capabilities to unseen specifications and diverse environments. This work proposes the first standardized benchmark that encompasses navigation and manipulation tasks of varying difficulty, supporting both static and dynamic environments, heterogeneous robot dynamics, and multimodal observations. The benchmark comprehensively captures the multidimensional complexity arising from tasks, environments, and perceptual inputs. Through large-scale empirical evaluation, the study reveals performance bottlenecks of current methods as specification and environmental complexity increase, and establishes a reproducible platform for comparative analysis along with clear directions for future research challenges.

Technology Category

• Natural Language Processing: Safety and Robustness
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• specification-guided reinforcement learning
• generalization
• linear temporal logic
• benchmark
• LTL-based RL

Innovation

• specification-guided reinforcement learning
• generalization benchmark
• linear temporal logic
• robotic manipulation
• navigation tasks