接触前実行監視：行動条件付き潜在世界モデルによるコンタクトガード
ContactGuard: Pre-Contact Execution Monitoring with Action-Conditioned Latent World Models
ロボットの接触を伴う操作において、接触前に失敗を予測して動作を中断する監視システムを提案。潜在空間での世界モデルを用いて、計画された行動の結果を予測し、失敗の可能性を検出する。
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著者: Gehan Zheng, Matthew Johnson-Roberson, Weiming Zhi
分類: cs.RO, cs.AI, cs.CV
原文アブストラクト
Contact-rich manipulation failures are often detected only after the robot has committed to contact. This is especially limiting in wrist-camera setups: close gripper--object views help observe contact, but a poor approach may already push, miss, slip, or disturb the object before conventional detectors react. We introduce \emph{ContactGuard}, a pre-contact execution monitor for chunked visuomotor policies. Given the policy's planned action chunk, ContactGuard predicts its short-horizon consequence in latent visual space and aborts if the predicted future latent indicates likely failure. Its latent world model is trained from unlabelled robot trajectories to predict compact multi-view visual embeddings under planned actions, avoiding pixel-level video prediction. A lightweight failure probe is then trained from a small labelled set of pre-contact clips. At deployment, ContactGuard anchors prediction before an imminent contact event, rolls the model forward under the policy's own actions, and verifies the predicted post-contact latent. Across real-world contact-rich manipulation tasks, ContactGuard predicts failure more accurately than direct and corrupted-action ablations, and transfers to live robot as a pre-contact abort signal without modifying the underlying policy.