PATCH: Action-Chunk-Conditioned Latent Patch Innovation Monitoring for Robot Manipulation
Learning-based manipulation policies have made substantial progress in real-world robot manipulation, particularly for short-horizon action generation. However, deployment in open workspaces remains fragile under unexpected local scene dynamics, such as moving objects, transient occlusions, or disturbances near the intended motion. Existing runtime monitors often rely on global observation anomalies, policy uncertainty, or frame-level visual changes, and struggle to distinguish task-relevant execution risk from benign visual variation. We introduce PATCH, an action-chunk-conditioned latent patch innovation monitor for deployment-time intervention. Given the active action chunk, PATCH defi...
| Authors | Yanan Zhou, Ranpeng Qiu, Yincong Chen et al. |
|---|---|
| Primary category | cs.RO |
| Published | 15 June 2026 |
| Last updated at source | 15 June 2026 |
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