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

2025-07-07

AI Summary

To address core challenges in robotic lifelong learning—catastrophic forgetting, insufficient knowledge reuse, and difficulty adapting to dynamic tasks—this paper proposes the Dynamic Retrieval-Augmented Expert Network (DRE-Net). DRE-Net integrates sparse-gated Mixture-of-Experts routing, parametric Retrieval-Augmented Generation (P-RAG), and a hierarchical reinforcement learning architecture comprising ReflexNet, SchemaPlanner, and HyperOptima. It further introduces the RSHO co-optimization mechanism to enable on-demand knowledge invocation and cross-task transfer. Evaluated on dynamic robotic manipulation tasks, DRE-Net achieves an average task success rate of 82.5%, outperforming the MoE baseline by 8.3 percentage points, while significantly reducing forgetting. These results demonstrate DRE-Net’s superior resource efficiency, strong generalization capability, and long-term stability in continual learning settings.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Machine Learning: Life-Long and Continual Learning
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

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.

Problem

• Addresses lifelong learning and catastrophic forgetting in robotics
• Enhances task adaptation via dynamic expert routing and retrieval
• Improves robotic performance with hierarchical reinforcement learning framework

Innovation

• Dynamic MoE gating for efficient routing
• Parametric RAG for knowledge enhancement
• Hierarchical RL for continuous adaptation