Dynamic Mixture of Progressive Parameter-Efficient Expert Library for Lifelong Robot Learning
arXiv:2506.05985v3 Announce Type: replace 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 isolate
The paper addresses a critical challenge in lifelong robot learning, which is becoming increasingly relevant as generalist AI agents and robotics mature and necessitate continuous adaptation.
This research provides a pathway for robots and AI systems to learn continuously and efficiently without forgetting, which is crucial for scalable and adaptable AI deployment in real-world scenarios.
The ability for AI systems, particularly in robotics, to robustly learn new tasks over extended periods while sharing knowledge and avoiding catastrophic forgetting is significantly advanced.
- · AI research institutions
- · Robotics manufacturers
- · Logistics and manufacturing sectors
- · AI agent developers
- · Companies relying on brittle single-task AI systems
More capable and adaptable autonomous robotic systems become feasible for widespread use.
Accelerates the development of general-purpose AI agents capable of operating in dynamic, unconstrained environments.
Contributes to the broader societal integration of AI and robotics, potentially impacting labor markets and human-robot interaction.
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Read at arXiv cs.LG