AI RESEARCH
Failure Identification in Imitation Learning Via Statistical and Semantic Filtering
arXiv CS.CV
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ArXi:2604.13788v1 Announce Type: cross Imitation learning (IL) policies in robotics deliver strong performance in controlled settings but remain brittle in real-world deployments: rare events such as hardware faults, defective parts, unexpected human actions, or any state that lies outside the