Efficient Continual Adaptation of Pretrained Robotic Policy with Online Meta-Learned Adapters

Continual adaptation is essential for general autonomous agents. For example, a household robot pretrained with a repertoire of skills must still adapt to unseen tasks specific to each household. Motivated by this, building upon parameter-efficient fine-tuning in language models, prior works have explored lightweight adapters to adapt pretrained policies, which can preserve learned features from the pretraining phase and demonstrate good adaptation performances. However, these approaches treat task learning separately, limiting knowledge transfer between tasks. In this paper, we propose Online Meta- Learned adapters (OMLA). Instead of applying adapters di- rectly, OMLA can facilitate knowledge transfer from previously learned tasks to current learning tasks through a novel meta- learning objective. Extensive experiments in both simulated and real-world environments demonstrate that OMLA can lead to better adaptation performances compared to the baseline methods.