基于检索加权局部适应的任务无关终身机器人学习
Task-agnostic Lifelong Robot Learning with Retrieval-based Weighted Local Adaptation
AI总结：
本文提出一种基于检索加权局部适应的任务无关终身机器人学习框架，通过经验回放与局部适应技术缓解灾难性遗忘，在无任务ID和边界信息下有效恢复知识，提升开放场景表现。
摘要：
智能机器人领域的一个基本目标是迈向终身学习机器人，使其能够随时间学习并适应未见场景。然而，持续学习新任务会因数据分布偏移引发灾难性遗忘问题。为缓解此问题，我们存储先前任务的部分数据，并以两种方式利用它：利用经验回放来保留已学技能，并应用一种新颖的基于检索的局部适应技术来恢复相关知识。由于终身学习机器人必须在无任务场景中运行，其中任务ID甚至任务边界均不可用，我们的方法在不依赖此类信息的情况下依然有效。我们还引入了一种选择性加权机制，以聚焦于最“被遗忘”的技能片段，确保有效的知识恢复。在多种操作任务上的实验结果表明，我们的框架为终身学习提供了可扩展的范式，提升了机器人在开放式、无任务场景中的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
