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

TL;DR

DRAE is a novel architecture combining dynamic expert routing, retrieval-augmented knowledge, and hierarchical reinforcement learning to improve lifelong learning and task adaptation in robotics, significantly reducing forgetting and enhancing task success.

Contribution

We introduce DRAE, integrating MoE, retrieval-augmented generation, and hierarchical RL for scalable lifelong learning in robotics, with novel routing and memory mechanisms.

Findings

Achieves 82.5% success rate on robotic tasks, outperforming baselines.

Maintains low catastrophic forgetting rates.

Effectively combines expert routing, retrieval, and hierarchical RL.

Abstract

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.