  The goal of Continual Learning (CL) task is to continuously learn multiple
new tasks sequentially while achieving a balance between the plasticity and
stability of new and old knowledge. This paper analyzes that this insufficiency
arises from the ineffective handling of outliers, leading to abnormal gradients
and unexpected model updates. To address this issue, we enhance the data-aware
and parameter-aware robustness of CL, proposing a Robust Continual Learning
(RCL) method. From the data perspective, we develop a contrastive loss based on
the concepts of uniformity and alignment, forming a feature distribution that
is more applicable to outliers. From the parameter perspective, we present a
forward strategy for worst-case perturbation and apply robust gradient
projection to the parameters. The experimental results on three benchmarks show
that the proposed method effectively maintains robustness and achieves new
state-of-the-art (SOTA) results. The code is available at:
https://github.com/HanxiXiao/RCL
