RIFAR: Reliability and Forgetting-Aware Replay for Continual Robot Learning
cs.AI
Submitted: 2026-10-02
Updated: 2026-10-10
Sources
- PHASER: Phase-Aware and Semantic Experience Replay for Vision-Language-Action Models
- World Action Models Enable Continual Imitation Learning with Recurrent Generative Replays
- LoRA: Low-Rank Adaptation of Large Language Models
- Simple Recipe Works: Vision-Language-Action Models are Natural Continual Learners with Reinforcement Learning
- LayerRoute: Action-Conditioned Mixture-of-Layers Routing for Vision-Language-Action Policies
- PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning
- CLARE: Continual Learning for Vision-Language-Action Models via Autonomous Adapter Routing and Expansion
- CRL-VLA: Continual Vision-Language-Action Learning
- Information-Theoretic Constraints for Continual Vision-Language-Action Alignment
- Can Vision-Language-Action Models Learn from Real-World Data Continually without Forgetting?
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