TIMID: Time-Dependent Mistake Detection in Videos of Robot Executions

• URL: http://arxiv.org/abs/2603.09782v1
• Date: Tue, 10 Mar 2026 15:17:34 GMT
• Title: TIMID: Time-Dependent Mistake Detection in Videos of Robot Executions
• Abstract summary: A new VAD-inspired architecture, TIMID, is able to detect robot time-dependent mistakes when executing high-level tasks.<n>Our architecture receives as inputs a video and prompts of the task and the potential mistake, and returns a frame-level prediction in the video of whether the mistake is present or not.
• Score: 39.788348066726826
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: As robotic systems execute increasingly difficult task sequences, so does the number of ways in which they can fail. Video Anomaly Detection (VAD) frameworks typically focus on singular, low-level kinematic or action failures, struggling to identify more complex temporal or spatial task violations, because they do not necessarily manifest as low-level execution errors. To address this problem, the main contribution of this paper is a new VAD-inspired architecture, TIMID, which is able to detect robot time-dependent mistakes when executing high-level tasks. Our architecture receives as inputs a video and prompts of the task and the potential mistake, and returns a frame-level prediction in the video of whether the mistake is present or not. By adopting a VAD formulation, the model can be trained with weak supervision, requiring only a single label per video. Additionally, to alleviate the problem of data scarcity of incorrect executions, we introduce a multi-robot simulation dataset with controlled temporal errors and real executions for zero-shot sim-to-real evaluation. Our experiments demonstrate that out-of-the-box VLMs lack the explicit temporal reasoning required for this task, whereas our framework successfully detects different types of temporal errors. Project: https://ropertunizar.github.io/TIMID/

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