Memory Anchors for Continual Robot Learning

2026-08-26

AI Summary

为解决机器人持续学习中新任务导致旧任务遗忘问题，研究提出记忆锚点方法，通过在重放缓冲区中保留关键旧任务数据来减少灾难性遗忘。

Abstract

Robot policies deployed in the wild should have the capability to continually learn new tasks without forgetting existing behaviors. A common approach to combat such catastrophic forgetting is to train on new task data with a replay buffer of previously learned task data. Although this buffer is commonly sampled randomly from all prior experiences, we show that a small set of these experiences contributes greatly in anchoring past performance. We call these experiences Memory Anchors. We identify Memory Anchors in regions where representations of new-task observations collapse onto those of old-task observations even though the tasks require conflicting actions, like when a familiar object must be manipulated in a new way. Rehearsing old data in this region plays a key role in preventing destructive overwriting of past task knowledge, serving as this critical Memory Anchor role. Excluding only 10% Memory Anchors before sampling the buffer leads to more than a 4.5x increase in catastrophic forgetting on the LIBERO benchmark suites. Conversely, enriching the replay buffer with Memory Anchors can decrease high-conflict task forgetting by 63% and enables successful continual learning of two task sequences on a real robot. Videos and additional visualizations can be found at https://robot-adaptation.github.io/MemoryAnchors

Problem

• Continual Learning
• Catastrophic Forgetting
• Robot Policies

Innovation

• Memory Anchors
• Continual Learning
• Catastrophic Forgetting
• Replay Buffer