arXiv 2610.130212026-10-12cs.AIcs.CLcs.CV

EmbodiedCLBench：评估自进化具身智能体的持续学习能力

EmbodiedCLBench: Evaluating Continual Learning for Self-Evolving Embodied Agents

School of Intelligence Science and Technology, Peking University（北京大学智能科学与技术学院）；State Key Laboratory of General Artificial Intelligence, BIGAI（北京通用人工智能研究院通用人工智能国家重点实验室）；University of Hong Kong（香港大学）

Jie Huang, Yanan Chen, Ruixun Liu, Kaichen He, Xinyi He, Rui Huang, Zilong Zheng, Zhenliang Zhang, Yiwu Zhong

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AI总结：

本研究推出首个具身模拟持续学习基准EmbodiedCLBench，经多实验发现当前智能体无法从额外经验提取有效信息，为具身自进化智能体设计提供资源。

摘要：

持续学习要求模型积累过往经验并将其迁移至新任务。尽管持续学习领域已有大量研究，但现有工作大多局限于非具身环境，未扩展至具身环境。为填补这一空白，我们推出EmbodiedCLBench，这是首个用于评估自进化智能体持续学习能力的具身模拟基准。我们以组合泛化为核心评估原则，该基准用于评估智能体是否能先从基础任务中学习，再将习得的技能重新组合以解决复杂、未见过的高级任务。基于EmbodiedCLBench，我们在多个智能体框架和学习方法上开展了大量实验，揭示了一致的局限性和独特的行为。结果表明，尽管在零样本设置下几乎无法解决高级任务，但基础任务的经验能实现持续改进；即使任务复杂度增加，这种经验带来的益处仍保持稳定。不过，这种改进并未随学习样本数量成比例扩大，当前智能体无法从额外经验中提取有意义的信息。总体而言，我们的发现强调持续学习是一项基础但未被充分探索的能力，而我们的基准为设计具身环境中有效的自进化智能体提供了宝贵资源。我们的代码和数据集可在该https URL获取。

英文摘要：

Continual learning requires models to accumulate past experience and transfer it to new tasks. Despite substantial research on continual learning, existing work remains largely confined to non-embodied settings, without extending to embodied environments. To address this gap, we introduce EmbodiedCLBench, the first benchmark in embodied simulation for evaluating the continual learning capability of self-evolving agents. With compositional generalization as the core evaluation principle, our benchmark evaluates whether agents can first learn from basic tasks and then recombine the acquired skills to solve complex, unseen advanced tasks. Based on EmbodiedCLBench, we conduct extensive experiments across multiple agent harnesses and learning methods, revealing consistent limitations and distinctive behaviors. Our results show that while advanced tasks can be hardly solved under zero-shot setting, the experience from basic tasks enables consistent improvement. Such benefit from the experience remains stable even if the task complexity increases. However, this improvement does not scale accordingly with the number of learning samples, and current agents cannot extract meaningful information from additional experience. Collectively, our findings highlight continual learning as a fundamental yet underexplored capability, and our benchmark offers valuable resources for designing effective self-evolving agents in embodied environments. Our code and dataset are available at https://pku-value-lab.github.io/EmbodiedCLBench-homepage.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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摘要：

英文摘要：

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