arXiv 2507.046612025-12-23cs.RO

DRAE：动态检索增强专家网络用于机器人终身学习与任务适应

DRAE: Dynamic Retrieval-Augmented Expert Networks for Lifelong Learning and Task Adaptation in Robotics

Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences（重庆绿色智能技术研究所，中国科学院）

Yayu Long, Kewei Chen, Long Jin, Mingsheng Shang

备注：: Accepted to the main conference of the Annual Meeting of the Association for Computational Linguistics (ACL 2025)

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AI总结：

DRAE通过动态检索增强专家网络实现机器人终身学习和任务适应，显著提升长期任务保留和知识重用能力。

摘要：

我们引入了动态检索增强专家网络（DRAE），这是一种突破性的架构，通过结合混合专家（MoE）的动态路由能力、检索增强生成（RAG）的知识增强力量、新颖的分层强化学习（RL）框架以及通过反射网络-模式规划器-超优化（RSHO）进行协调，解决了终身学习、灾难性遗忘和任务适应的挑战。DRAE通过稀疏MoE门控机制动态路由专家模型，实现高效的资源分配，同时通过参数检索（P-RAG）利用外部知识来增强学习过程。我们提出了一种新的RL框架，包含反射网络用于低层任务执行，模式规划器用于符号推理，以及超优化用于长期上下文建模，确保持续适应和记忆保留。实验结果表明，DRAE在长期任务保留和知识重用方面显著优于基线方法，实现了在一组动态机器人操作任务上的平均任务成功率82.5%，相比传统MoE模型的74.2%。此外，DRAE保持了极低的遗忘率，在灾难性遗忘缓解方面优于最先进的方法。这些结果展示了我们的方法在实现机器人灵活、可扩展和高效终身学习方面的有效性。

英文摘要：

We introduce Dynamic Retrieval-Augmented Expert Networks (DRAE), a groundbreaking architecture that addresses the challenges of lifelong learning, catastrophic forgetting, and task adaptation by combining the dynamic routing capabilities of Mixture-of-Experts (MoE); leveraging the knowledge-enhancement power of Retrieval-Augmented Generation (RAG); incorporating a novel hierarchical reinforcement learning (RL) framework; and coordinating through ReflexNet-SchemaPlanner-HyperOptima (RSHO).DRAE dynamically routes expert models via a sparse MoE gating mechanism, enabling efficient resource allocation while leveraging external knowledge through parametric retrieval (P-RAG) to augment the learning process. We propose a new RL framework with ReflexNet for low-level task execution, SchemaPlanner for symbolic reasoning, and HyperOptima for long-term context modeling, ensuring continuous adaptation and memory retention. Experimental results show that DRAE significantly outperforms baseline approaches in long-term task retention and knowledge reuse, achieving an average task success rate of 82.5% across a set of dynamic robotic manipulation tasks, compared to 74.2% for traditional MoE models. Furthermore, DRAE maintains an extremely low forgetting rate, outperforming state-of-the-art methods in catastrophic forgetting mitigation. These results demonstrate the effectiveness of our approach in enabling flexible, scalable, and efficient lifelong learning for robotics.

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