The pursuit of long-term autonomy mandates that robotic agents must continuously adapt to their changing environments and learn to solve new tasks. Continual learning seeks to overcome the challenge of catastrophic forgetting, where learning to solve new tasks causes a model to forget previously learnt information. Prior-based continual learning methods are appealing for robotic applications as they are space efficient and typically do not increase in computational complexity as the number of tasks grows. Despite these desirable properties, prior-based approaches typically fail on important benchmarks and consequently are limited in their potential applications compared to their memory-based counterparts. We introduce Bayesian adaptive moment regularization (BAdam), a novel prior-based method that better constrains parameter growth, leading to lower catastrophic forgetting. Our method boasts a range of desirable properties for robotic applications such as being lightweight and task label-free, converging quickly, and offering calibrated uncertainty that is important for safe real-world deployment. Results show that BAdam achieves state-of-the-art performance for prior-based methods on challenging single-headed class-incremental experiments such as Split MNIST and Split FashionMNIST, and does so without relying on task labels or discrete task boundaries.

翻译：追求长期自主性要求机器人智能体必须持续适应不断变化的环境，并学会解决新任务。持续学习旨在克服灾难性遗忘的挑战——即学习新任务会导致模型遗忘先前习得的信息。基于先验的持续学习方法因其空间效率高且计算复杂度通常不随任务数量增长的特点，在机器人应用中颇具吸引力。尽管具有这些理想特性，但与基于记忆的方法相比，基于先验的方法通常在重要基准测试中表现不佳，因而限制了其潜在应用。我们提出贝叶斯自适应矩正则化（BAdam），一种新型先验方法，该方法能更有效地约束参数增长，从而降低灾难性遗忘程度。我们的方法具备一系列适合机器人应用的理想特性，包括轻量化、无任务标签需求、快速收敛，以及提供校准后的不确定性估计——这对安全现实部署至关重要。实验结果表明，在具有挑战性的单头类增量实验（如Split MNIST和Split FashionMNIST）中，BAdam在基于先验的方法中取得了最先进性能，且无需依赖任务标签或离散任务边界。