Memory Anchors for Continual Robot Learning
- Published
- Source
- arXiv
- Paper number
- 1034
- Field
- Robotics
- arXiv ID
- 2608.26545
Key points
- It found that not all data in a replay buffer are equally important: a small subset in conflict regions determines how well forgetting is prevented.
- It developed a three-stage procedure for selecting memory anchors by locating regions in the policy's latent space where observations overlap but actions conflict.
- Removing only the top 10% of anchors increased forgetting 4.5-fold on the LIBERO benchmark.
- ANCHORER, which fills 10% of the buffer with anchors, reduced forgetting on conflicting tasks by 63%.
- In continual learning over a sequence of two real-robot tasks, it achieved 1.7 times the overall success rate of a random buffer.
Paper links
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