ContactGuard: Pre-Contact Execution Monitoring with Action-Conditioned Latent World Models

ContactGuard: Pre-Contact Execution Monitoring with Action-Conditioned Latent World Models

|Authors: Gehan Zheng, Matthew Johnson-Roberson, Weiming Zhi

|Venue: arXiv preprint | Year: 2026

|arXiv: 2608.13438v1

Abstract

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.

Key Contributions

• 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 cont…
• 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 l…
• Its latent world model is trained from unlabelled robot trajectories to predict compact multi-vie…

Topics

• manipulation
• vision
• reinforcement-learning

Code & Data

No code repository linked in paper metadata.

BibTeX

@article{Zheng2026_260813438v1,
  title     = {ContactGuard: Pre-Contact Execution Monitoring with Action-Conditioned Latent World Models},
  author    = {Gehan Zheng and Matthew Johnson-Roberson and Weiming Zhi},
  year      = {2026},
  eprint    = {2608.13438v1},
  archivePrefix = {arXiv},
  primaryClass  = {cs.RO},
  url       = {https://arxiv.org/abs/2608.13438v1}
}