标题： DRAE：用于机器人终身学习和任务适应的动态检索增强专家网络 显示英文标题

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

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，通过结合专家混合（MoE）的动态路由能力；利用检索增强生成（RAG）的知识增强能力；引入一种新的分层强化学习（RL）框架；并通过ReflexNet-SchemaPlanner-HyperOptima（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，同时通过参数化检索（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，包括ReflexNet用于低级任务执行，SchemaPlanner用于符号推理，HyperOptima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，在一组动态机器人操作任务中实现了82.5%的平均任务成功率，相比之下传统MoE模型为74.2%。此外，DRAE保持极低的遗忘率，在灾难性遗忘缓解方面优于最先进的方法。这些结果证明了我们的方法在为机器人实现灵活、可扩展和高效的终身学习方面的有效性。 显示英文摘要

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.