Paper
SafeGen-LLM: Enhancing Safety Generalization in Task Planning for Robotic Systems
Authors
Jialiang Fan, Weizhe Xu, Mengyu Liu, Oleg Sokolsky, Insup Lee, Fangxin Kong
Abstract
Safety-critical task planning in robotic systems remains challenging: classical planners suffer from poor scalability, Reinforcement Learning (RL)-based methods generalize poorly, and base Large Language Models (LLMs) cannot guarantee safety. To address this gap, we propose safety-generalizable large language models, named SafeGen-LLM. SafeGen-LLM can not only enhance the safety satisfaction of task plans but also generalize well to novel safety properties in various domains. We first construct a multi-domain Planning Domain Definition Language 3 (PDDL3) benchmark with explicit safety constraints. Then, we introduce a two-stage post-training framework: Supervised Fine-Tuning (SFT) on a constraint-compliant planning dataset to learn planning syntax and semantics, and Group Relative Policy Optimization (GRPO) guided by fine-grained reward machines derived from formal verification to enforce safety alignment and by curriculum learning to better handle complex tasks. Extensive experiments show that SafeGen-LLM achieves strong safety generalization and outperforms frontier proprietary baselines across multi-domain planning tasks and multiple input formats (e.g., PDDLs and natural language).
Metadata
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2602.24235v1</id>\n <title>SafeGen-LLM: Enhancing Safety Generalization in Task Planning for Robotic Systems</title>\n <updated>2026-02-27T18:06:10Z</updated>\n <link href='https://arxiv.org/abs/2602.24235v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2602.24235v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>Safety-critical task planning in robotic systems remains challenging: classical planners suffer from poor scalability, Reinforcement Learning (RL)-based methods generalize poorly, and base Large Language Models (LLMs) cannot guarantee safety. To address this gap, we propose safety-generalizable large language models, named SafeGen-LLM. SafeGen-LLM can not only enhance the safety satisfaction of task plans but also generalize well to novel safety properties in various domains. We first construct a multi-domain Planning Domain Definition Language 3 (PDDL3) benchmark with explicit safety constraints. Then, we introduce a two-stage post-training framework: Supervised Fine-Tuning (SFT) on a constraint-compliant planning dataset to learn planning syntax and semantics, and Group Relative Policy Optimization (GRPO) guided by fine-grained reward machines derived from formal verification to enforce safety alignment and by curriculum learning to better handle complex tasks. Extensive experiments show that SafeGen-LLM achieves strong safety generalization and outperforms frontier proprietary baselines across multi-domain planning tasks and multiple input formats (e.g., PDDLs and natural language).</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.RO'/>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.AI'/>\n <published>2026-02-27T18:06:10Z</published>\n <arxiv:comment>12 pages, 6 figures</arxiv:comment>\n <arxiv:primary_category term='cs.RO'/>\n <author>\n <name>Jialiang Fan</name>\n </author>\n <author>\n <name>Weizhe Xu</name>\n </author>\n <author>\n <name>Mengyu Liu</name>\n </author>\n <author>\n <name>Oleg Sokolsky</name>\n </author>\n <author>\n <name>Insup Lee</name>\n </author>\n <author>\n <name>Fangxin Kong</name>\n </author>\n </entry>"
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