視覚言語行動モデルは実世界データから忘れずに継続学習できるか？
Can Vision-Language-Action Models Learn from Real-World Data Continually without Forgetting?
実世界のロボット連続操作タスクでVLAモデルの継続学習を検証し、経験再生が破滅的忘却を防ぎ、ジョイント訓練と同等以上の性能を達成することを示した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Jiarun Zhu, Yijun Hong, Xiaoquan Sun, Zetian Xu, Qijun He, Haijier Chen, Zhiyong Wang, Mingqi Yuan, Wenjun Zeng, Jiayu Chen
分類: cs.RO
原文アブストラクト
Vision-Language-Action (VLA) models provide a promising foundation for general-purpose robotics, yet their real-world deployment demands the ability to continually acquire new skills without forgetting prior ones. While recent studies have explored continual learning for VLA models in simulated settings, the challenge remains largely unexamined under realistic physical conditions. To bridge this gap, we construct a real-world continual learning benchmark comprising ten diverse sequential manipulation tasks across both single-arm and bimanual configurations. Through extensive experiments on this benchmark, we find that naive sequential fine-tuning leads to severe catastrophic forgetting, whereas a well-configured experience replay (ER) approach can effectively mitigate forgetting and outperform joint multi-task training under equivalent computational budgets. Notably, by synthesizing our empirical findings, we successfully achieve stable continual learning across the full 10-task heterogeneous stream, retaining previously acquired capabilities while adapting to diverse new skills in real-world deployment. This work presents an empirical study grounded in real-world continual VLA learning and offers actionable insights for deploying robust, long-lived robotic policies.
