让机器人在无任务标识下持续学习，避免遗忘新旧技能。
Task-agnostic Lifelong Robot Learning with Retrieval-based Weighted Local Adaptation
- 用检索式局部自适应恢复遗忘知识，无需任务标签。
- 通过选择性加权聚焦最易遗忘的技能片段，提升恢复效果。
- 在多种操作任务中验证，适合开放场景的长期学习。
智能机器人的重要目标是实现持续学习，能够在时间推移中适应未见过的场景。然而，持续学习新任务会因数据分布变化导致灾难性遗忘。为此，我们存储过往任务的部分数据，以两种方式利用：一是通过经验回放保留已学技能，二是采用新颖的基于检索的局部自适应技术恢复相关知识。由于终身学习机器人需在无任务标识的场景中运行，任务编号与边界均不可知，本方法无需依赖此类信息即可有效工作。同时引入选择性加权机制，聚焦最易遗忘的技能段，确保知识恢复高效。在多样化的操控任务上进行实验，结果表明该框架为终身学习提供了可扩展范式，在开放式、无任务约束场景中显著提升机器人性能。
原文摘要 · Abstract (English)
A fundamental objective in intelligent robotics is to move towards lifelong learning robot that can learn and adapt to unseen scenarios over time. However, continually learning new tasks would introduce catastrophic forgetting problems due to data distribution shifts. To mitigate this, we store a subset of data from previous tasks and utilize it in two manners: leveraging experience replay to retain learned skills and applying a novel Retrieval-based Local Adaptation technique to restore relevant knowledge. Since a lifelong learning robot must operate in task-free scenarios, where task IDs and even boundaries are not available, our method performs effectively without relying on such information. We also incorporate a selective weighting mechanism to focus on the most "forgotten" skill segment, ensuring effective knowledge restoration. Experimental results across diverse manipulation tasks demonstrate that our framework provides a scalable paradigm for lifelong learning, enhancing robot performance in open-ended, task-free scenarios.
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