KITE: Keyframe-Indexed Tokenized Evidence for VLM-Based Robot Failure Analysis
We present KITE, a training-free, keyframe-anchored, layout-grounded front-end that converts long robot-execution videos into compact, interpretable tokenized evidence for vision-language models (VLMs). KITE distills each trajectory into a small set of motion-salient keyframes with open-vocabulary detections and pairs each keyframe with a schematic bird's-eye-view (BEV) representation that encodes relative object layout, axes, timestamps, and detection confidence. These visual cues are serialized with robot-profile and scene-context tokens into a unified prompt, allowing the same front-end to support failure detection, identification, localization, explanation, and correction with an off-...
| Authors | Mehdi Hosseinzadeh, King Hang Wong, Feras Dayoub |
|---|---|
| Primary category | cs.RO |
| Published | 8 April 2026 |
| Last updated at source | 8 April 2026 |
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