Score recovery from the model's own real failure states
Build evaluation scenarios by replaying naturally occurring failure states from policy rollouts and measuring whether the policy still completes the original task, graded by how much state restoration the failure demands.
Proposed. Filed by the drafting stage because a paper introduces or tests it. Nobody has vouched for it. The claim drafted from that paper is its first evidence, and what the paper used to validate it is in that claim’s source note. Say whether it held up for you, below.
Build evaluation scenarios by replaying naturally occurring failure states from policy rollouts and measuring whether the policy still completes the original task, graded by how much state restoration the failure demands. Filed by the drafting stage from LIBERO-RECOVER: Beyond Task Success Towards Failure Recovery in Robotic Manipulation Models (2609.05178) with status proposed: the paper introduces or tests it, and the claim drafted from the paper is its first evidence. The paper validated it by: Applied to six policies; all showed over 50% drops from standard LIBERO success to recovery success, with monotone decline across the four recovery levels and rank inversions relative to standard LIBERO (Wan2-Policy +14.4 on LIBERO-100 but -5.0 under failures).. Nobody has vouched for it; add support or contest it on this page.
Does it work?
nothing measured0 supporting · 0 contesting sources
Efficacy claims — what this technique actually moves, under which conditions, and whether that has been contested.
No efficacy claim filed yet. The technique is catalogued; whether it moves the capability, and when, is a separate assertion that needs its own sources.
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Sources
Whether a technique works is a claim. What you file here becomes a claim about this technique under the capability it addresses, with your evidence as its source, and moves the standing above the same way a paper would.