Sentinel-VLA: 動的推論とエラー回復のための能動的状態監視を備えたメタ認知VLAモデル
Sentinel-VLA: A Metacognitive VLA Model with Active Status Monitoring for Dynamic Reasoning and Error Recovery
VLAモデルに能動的な状態監視モジュールを追加し、必要時のみ動的推論やエラー回復を行うメタ認知型モデルを提案。自己進化的継続学習と直交アダプタにより性能向上と破滅的忘却防止を実現し、実機で成功率を30%以上向上させた。
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著者: Wenhao Li, Xiu Su, Dan Niu, Yichao Cao, Hongyan Xu, Zhe Qu, Lei Fan, Shan You, Chang Xu
分類: cs.RO
原文アブストラクト
Vision-language-action (VLA) models have advanced the field of embodied manipulation by harnessing broad world knowledge and strong generalization. However, current VLA models still face several key challenges, including limited reasoning capability, lack of status monitoring, and difficulty in self-correction. In this paper, we introduce \textbf{Sentinel-VLA}, a metacognitive VLA model equipped with an active ``sentinel'' module to monitor real-time execution status. Only when necessary, such as during initial planning or upon detecting an error, the model triggers a dynamic reasoning or formulate error recovery solutions. This on-demand reasoning mechanism ensures robust decision-making while minimizing computational overhead. Notably, all training data (spanning 44 tasks and over 2.6 million transitions) is automatically generated and annotated through our designed pipeline. We also propose the Self-Evolving Continual Learning (SECL) algorithm, which allows Sentinel-VLA to identify its capability boundaries and automatically collect data for expansion, paired with Orthogonal Continual Adapter (OC-Adapter) to constrain parameter updates to an orthogonal space, thereby preventing catastrophic forgetting. Real-world experiments demonstrate that Sentinel-VLA boosts the task success rate by over 30\% compared to the SOTA model, PI0. We will open-source all the code, weights, and data generation pipeline.
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