基盤世界モデルによる両腕マニピュレータの故障検出の高精度化
Foundational World Models Accurately Detect Bimanual Manipulator Failures
事前学習済み視覚基盤モデルの潜在空間で確率的な世界モデルを学習し、不確実性を故障検出のスコアとして利用するランタイムモニタを開発。シミュレーションと新規データセットで既存手法を大幅に上回る性能を示した。
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？
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6. 次に読むべき論文は？
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著者: Isaac R. Ward, Michelle Ho, Houjun Liu, Aaron Feldman, Joseph Vincent, Liam Kruse, Sean Cheong, Duncan Eddy, Mykel J. Kochenderfer, Mac Schwager
分類: cs.RO, cs.AI
原文アブストラクト
Deploying visuomotor robots at scale is challenging due to the potential for anomalous failures to degrade performance, cause damage, or endanger human life. Bimanual manipulators are no exception; these robots have vast state spaces comprised of high-dimensional images and proprioceptive signals. Explicitly defining failure modes within such state spaces is infeasible. In this work, we overcome these challenges by training a probabilistic, history informed, world model within the compressed latent space of a pretrained vision foundation model (NVIDIA's Cosmos Tokenizer). The model outputs uncertainty estimates alongside its predictions that serve as non-conformity scores within a conformal prediction framework. We use these scores to develop a runtime monitor, correlating periods of high uncertainty with anomalous failures. To test these methods, we use the simulated Push-T environment and the Bimanual Cable Manipulation dataset, the latter of which we introduce in this work. This new dataset features trajectories with multiple synchronized camera views, proprioceptive signals, and annotated failures from a challenging data center maintenance task. We benchmark our methods against baselines from the anomaly detection and out-of-distribution detection literature, and show that our approach considerably outperforms statistical techniques. Furthermore, we show that our approach requires approximately one twentieth of the trainable parameters as the next-best learning-based approach, yet outperforms it by 3.8% in terms of failure detection rate, paving the way toward safely deploying manipulator robots in real-world environments where reliability is non-negotiable.
