用小模型实现高效社交导航，兼顾速度与行为合规性。
E-SocialNav: Efficient Socially Compliant Navigation with Language Models
- 采用两阶段训练：监督微调+直接偏好优化
- 小数据训练下超越零样本基线，行为准确率更高
- 适合资源受限机器人实时部署，响应快能耗低
语言模型在机器人导航中应用日益广泛，但现有评估基准多关注导航成功率，忽视社交合规性。大型语言模型计算开销大，响应慢、耗能高，难以在资源受限的机器人平台实现实时部署。本文评估了GPT-4o和Claude在导航中的社交合规性，并提出E-SocialNav——一种高效社交合规导航语言模型。尽管训练数据量较小，E-SocialNav在生成社交合规行为方面持续优于零样本基线。通过监督微调与直接偏好优化相结合的两阶段训练流程，该模型在文本语义相似度（对比人工标注）和动作准确性上均表现优异。代码已开源。
原文摘要 · Abstract (English)
Language models (LMs) are increasingly applied to robotic navigation; however, existing benchmarks primarily emphasize navigation success rates while paying limited attention to social compliance. Moreover, relying on large-scale LMs can raise efficiency concerns, as their heavy computational overhead leads to slower response times and higher energy consumption, making them impractical for real-time deployment on resource-constrained robotic platforms. In this work, we evaluate the social compliance of GPT-4o and Claude in robotic navigation and propose E-SocialNav, an efficient LM designed for socially compliant navigation. Despite being trained on a relatively small dataset, E-SocialNav consistently outperforms zero-shot baselines in generating socially compliant behaviors. By employing a two-stage training pipeline consisting of supervised fine-tuning followed by direct preference optimization, E-SocialNav achieves strong performance in both text-level semantic similarity to human annotations and action accuracy. The source code is available at https://github.com/Dr-LingXiao/ESocialNav.
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