Source assessmentSupporting
Failure Detection for Surgical Robot Imitation Policies via Flow-Matching World Modeling
surgical robot failure detectionAcademic
Assessment outcome
total 3/6 (actionability 0 + authority 1 + currency 2)
Total3/6
Actionability0/2
Authority1/2
Currency2/2
Authorship & scope
Attribution (G4 evidence)
No evidence recorded for this source.
Issuer authority (evidence)
No evidence recorded for this source.
Issuers, who it governs, legal force and content are on the full record.
Eligibility gates
G1 · Retrievablepass
Passed in screening.
G2 · AI nexuspass
Passed in screening.
G3 · Governance nexuspass
Passed in screening.
G4 · Attributablepass
Passed in screening.
G5 · In forcepass
Passed in screening.
Scored criteria
Actionability
0/2No rationale recorded.
To address these challenges, we introduce FoMo- FD (Flow-Matching World Model for Failure Detection), a failure detection method that learns nominal short-horizon visual dynamics with an action-conditioned flow-matching world model.
Authority
1/2No rationale recorded.
This work has been submitted to the IEEE for possible publication.
Currency
2/2No rationale recorded.
Classification
Coverage topicsurgical robot failure detection
PerspectiveAcademic
Lifecycle stage6 Operate & monitor
Layermodel
GranularityNot recorded