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

Source: arXiv - 2604.24729v1

Overview

SpecRLBench is a new benchmark that puts specification‑guided reinforcement learning (RL) to the test. By framing tasks with linear temporal logic (LTL) formulas, the benchmark measures how well modern RL agents can generalize to unseen specifications and environments—something that matters when you want a single policy to handle many real‑world robot tasks.

Key Contributions

• A unified benchmark suite covering navigation and manipulation, with static & dynamic scenes, multiple robot dynamics, and different sensor modalities.
• Four difficulty tiers that systematically increase the complexity of LTL specifications (from simple reach‑goal to nested temporal constraints).
• Comprehensive evaluation protocol including zero‑shot specification transfer, few‑shot fine‑tuning, and cross‑domain generalization.
• Open‑source implementation (Python, Gym‑compatible) and a leaderboard to encourage reproducible comparisons.
• Empirical analysis of several state‑of‑the‑art LTL‑guided RL methods, exposing where they succeed and where they break down.

Methodology

1. Task encoding with LTL – Each robot task is expressed as an LTL formula (e.g., “eventually visit A and always avoid B”). The formula is compiled into a deterministic finite automaton (DFA) that augments the RL state space.
2. Environment families – The benchmark provides a collection of Gym‑style environments:
   • Navigation: grid worlds, continuous mazes, and dynamic obstacle courses.
   • Manipulation: pick‑place tables, drawer opening, and tool‑use scenarios.
3. Training regimes – Researchers can train agents on a subset of specifications (the “source” set) and then evaluate on a held‑out “target” set that varies in logical structure and environment layout.
4. Metrics – Success rate, sample efficiency (episodes to 90 % success), and specification compliance (percentage of LTL constraints satisfied) are reported per difficulty level.
5. Baseline algorithms – The authors benchmarked three representative approaches: (a) reward shaping from LTL, (b) product‑MDP RL with DFA, and (c) hierarchical policy networks that condition on the parsed formula.

Results & Findings

• General trend: All methods degrade sharply as specifications become more nested and environments more dynamic.
• Hierarchical conditioning on the parsed LTL yields the best zero‑shot transfer, but still requires substantial fine‑tuning for the hardest tier.
• Sample efficiency suffers dramatically on the “very hard” level, indicating that current exploration strategies struggle with the compounded state‑space explosion introduced by the DFA product.

Practical Implications

• Robotics pipelines: Engineers can use SpecRLBench to evaluate whether a policy trained on a handful of demo tasks will reliably handle new, safety‑critical specifications (e.g., “always keep a safe distance from humans while delivering a package”).
• Product development: The benchmark’s modular design lets teams plug in their own perception stacks (camera, LiDAR) and robot dynamics, making it a realistic testbed before field deployment.
• Tooling for developers: Because the suite is Gym‑compatible and includes ready‑made wrappers for popular RL libraries (Stable‑Baselines3, RLlib), integrating it into CI pipelines for regression testing of specification‑aware agents is straightforward.
• Accelerating research‑to‑industry transfer: By exposing the exact failure modes (e.g., inability to satisfy “always avoid moving obstacles” in dynamic scenes), developers can prioritize improvements such as better curriculum learning or model‑based planning components.

Limitations & Future Work

• Scalability of DFA products: The current implementation can become memory‑intensive for deeply nested LTL formulas, limiting the benchmark to relatively short specifications.
• Limited real‑world validation: All environments are simulated; bridging the sim‑to‑real gap (e.g., sensor noise, actuator lag) remains an open challenge.
• Specification language scope: Only LTL is supported; extending to richer logics (e.g., Signal Temporal Logic) could capture more nuanced timing constraints.
• Future directions suggested by the authors include hierarchical curriculum generation, meta‑learning across specifications, and integrating model‑based planners to alleviate exploration bottlenecks.

Authors

• Zijian Guo
• İlker Işık
• H. M. Sabbir Ahmad
• Wenchao Li

Paper Information

• arXiv ID: 2604.24729v1
• Categories: cs.LG
• Published: April 27, 2026
• PDF: Download PDF