Continual/lifelong learning for long-term robotic operation

Establish practical continual or lifelong learning methods for robotics that enable robots to operate over long periods while adapting to new tasks and environments without sacrificing previously acquired capabilities, thereby overcoming catastrophic forgetting during sequential task acquisition.

Background

The paper studies continual learning for vision-language-action (VLA) models in robotics, where robots must acquire new skills over time without losing previously learned abilities. Naive sequential fine-tuning of pre-trained VLAs causes catastrophic forgetting, making it unsuitable for long-term deployment.

Existing approaches often rely on experience replay, regularization, or architectural changes, but they have practical drawbacks such as storage requirements for exemplars, reliance on task identifiers, or limited scalability for long task sequences. This motivates the identification of continual and lifelong learning in robotics as an open challenge, which the paper addresses with the proposed CLARE framework.