Auditing Instruction-Trajectory Mismatches in Multimodal Robot Demonstrations
Robot demonstration datasets used to train vision-language-action policies can contain a subtle but harmful failure mode: trajectories that are behaviorally correct but paired with the wrong language instruction. We study post-hoc auditing of these Instruction-Trajectory Mismatches (ITMs). Unlike failed rollouts, ITMs often look plausible, and can corrupt the language-behavior mapping learned by the policy. We propose Multimodal Probabilistic Fusion (MMPF), a training-free auditing framework that treats each modality as an expert, estimates a task-label distribution from local neighborhood agreement and global prototype similarity, and then fuses modalities with predictive-entropy weighti...
| Authors | Simon Holk, Ryosuke Takanami, Tatsuya Matsushima et al. |
|---|---|
| Primary category | cs.RO |
| Published | 8 August 2026 |
| Last updated at source | 8 August 2026 |
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