CASH: Calibrated Self-Healing Vision-Language-Action Policies for Embodied Agents

Action-chunking vision-language-action (VLA) policies predict multiple future actions and execute them without incorporating new observations, local execution errors can thus accumulate over the open-loop horizon. To address this issue, we present CASH (Calibrated Self-Healing), a lightweight failure-detection and self-healing framework that keeps the VLA frozen and consists of detection and self-healing. The detection module contains an execution anomaly detector (EAD) and a policy anomaly detector (PAD). EAD tests whether an executed action is consistent with the observed latent transition. Its score combines forward prediction error, directional disagreement, and inverse-dynamics reconstruction error; a conformalized quantile threshold with a one-sided CUSUM (cumulative sum) that converts the score into a state-conditioned trigger for persistent deviations. PAD tests whether a proposed action is plausible under the current observation and instruction, using successful actions and several families of counterfactual actions to train a value model. When EAD triggers, CASH computes the latent displacement that should have occurred but has not yet occurred, and adjusts the action chunk through a lightweight dynamics model. When PAD triggers, CASH shortens the action chunk and triggers earlier re-planning; repeated low scores activate a window reset for severe out-of-distribution (OOD) conditions. The detection modules are trained on cached latent features using successful trajectories only, without failure data or manual failure labels. The self-healing module does not require additional training. CASH can be integrated into different VLA models without retraining the VLA backbone, while preserving the efficiency of action chunking.