TACO: 触覚を考慮した世界モデルによるスケーラブルなVLAポストトレーニングの自己修正
TACO: TActile World Model as a Self-COrrector forScalable VLA Post-Training
接触を伴う操作タスクで失敗するVLAモデルに対し、触覚を考慮した世界モデルを用いて失敗状態を認識し、修正動作を想像・ラベル付けすることで、人間の介入なしにポリシーを改善するフレームワークを提案した。
詳しい要約
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著者: Shengbang Liu, Yueru Jia, Yuyang Yan, Jiaming Liu, Xinran Zhang, Qiuxuan Feng, Yandong Guo, Shiji Zhou, Boxin Shi, Shanghang Zhang
分類: cs.RO
原文アブストラクト
Vision-Language-Action (VLA) models have shown promising generalization in robotic manipulation, but they still struggle with contact-rich tasks, where minor contact perturbations can cause unrecoverable failures that are hard to detect from vision alone. Since these failures are localized rather than task-level semantic errors, tactile-aware corrective post-training offers an efficient way to improve recovery. However, scaling such supervision through human intervention is costly. Recent works have explored world models to synthesize imagined rollouts for policy improvement, but vision-only world models may produce visually plausible yet contact-inconsistent trajectories. We therefore introduce TACO, a tactile-aware world-model-driven framework for scalable VLA post-training in contact-rich manipulation. Given real robot rollouts, TACO follows a Recognize-Imagine-Label loop with a tactile-aware world model: a unified progress-action model recognizes failure-adjacent states using progress estimates, a visuo-tactile generation model imagines local correction segments, and the progress-action model labels them with executable corrective actions. To incorporate tactile corrective supervision into VLA post-training, TACO combines knowledge-insulated tactile adaptation with advantage-conditioned training, enabling the policy to learn from imagined corrections without degrading pretrained visual-language priors. These components enable TACO to convert real-world failures into imagined visuo-tactile corrections for iterative VLA post-training. Experiments on real-world contact-rich manipulation tasks show that TACO achieves 44% absolute success rate improvement over the base policy and 32% over the policy without knowledge-insulated tactile adaptation.
関連論文
- ViTaR: 基盤VLA操作のための視触覚残差適応VLA/触覚/操作
- τ: 未来の視覚的監督から学習する触覚拡張型視覚言語行動モデルVLA/触覚/操作
- N0-VTLA：潜在触覚トークンによる視覚・触覚・言語・行動モデルのスケーリングVLA/触覚/操作
- AT-VLA: 適応的触覚注入による視覚言語行動モデルのフィードバック反応向上VLA/触覚/操作
- TacVLA: 接触認識型触覚融合による堅牢な視覚言語行動操作VLA/触覚/操作
