Dynamic Mixture of Progressive Parameter-Efficient Expert Library for Lifelong Robot Learning

• URL: http://arxiv.org/abs/2506.05985v1
• Date: Fri, 06 Jun 2025 11:13:04 GMT
• Title: Dynamic Mixture of Progressive Parameter-Efficient Expert Library for Lifelong Robot Learning
• Authors: Yuheng Lei, Sitong Mao, Shunbo Zhou, Hongyuan Zhang, Xuelong Li, Ping Luo,
• Abstract summary: A generalist agent must continuously learn and adapt throughout its lifetime, achieving efficient forward transfer while minimizing catastrophic forgetting.<n>Previous work has explored parameter-efficient fine-tuning for single-task adaptation, effectively steering a frozen pretrained model with a small number of parameters.<n>We propose Dynamic Mixture of Progressive Efficient Expert Library (DMPEL) for lifelong robot learning.<n>Our framework outperforms state-of-the-art lifelong learning methods in success rates across continual adaptation, while utilizing minimal trainable parameters and storage.
• Score: 69.81148368677593
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A generalist agent must continuously learn and adapt throughout its lifetime, achieving efficient forward transfer while minimizing catastrophic forgetting. Previous work within the dominant pretrain-then-finetune paradigm has explored parameter-efficient fine-tuning for single-task adaptation, effectively steering a frozen pretrained model with a small number of parameters. However, in the context of lifelong learning, these methods rely on the impractical assumption of a test-time task identifier and restrict knowledge sharing among isolated adapters. To address these limitations, we propose Dynamic Mixture of Progressive Parameter-Efficient Expert Library (DMPEL) for lifelong robot learning. DMPEL progressively learn a low-rank expert library and employs a lightweight router to dynamically combine experts into an end-to-end policy, facilitating flexible behavior during lifelong adaptation. Moreover, by leveraging the modular structure of the fine-tuned parameters, we introduce coefficient replay to guide the router in accurately retrieving frozen experts for previously encountered tasks, thereby mitigating catastrophic forgetting. This method is significantly more storage- and computationally-efficient than applying demonstration replay to the entire policy. Extensive experiments on the lifelong manipulation benchmark LIBERO demonstrate that our framework outperforms state-of-the-art lifelong learning methods in success rates across continual adaptation, while utilizing minimal trainable parameters and storage.

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