Unverified50% confidenceCautionExact time
Domain randomization can only cover the types of disturbance the designers anticipated, and long-tail situations in the real environment such as loose handrails, slippery floors, and sudden lighting changes may still fall outside the training distribution.
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50%
Confidence
Long-term
Relevance
10/26/2026
First Seen
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Related Claims
UnverifiedDomain randomization can only cover error sources that the developer has already identified and explicitly modeled; once the real robot exhibits unmodeled factors, the policy is exposed to training blind spots.79% similarUnverifiedEdge cases lie at the boundary of or outside the Operational Design Domain (ODD), where systems may exhibit unexpected behavior due to sensor degradation, scenario sparsity, or training distribution shift.71% similarVerifiedExcessive domain randomization can cause policies to become overly conservative, and certain real-world phenomena (such as deformation of flexible objects) are difficult to cover through simple parameter randomization.70% similarUnverifiedPolicies lacking domain randomization typically perform well only in the single simulator used for training and quickly fail when confronted with the physical uncertainties of a real robot.70% similarUnverified过度域随机化会引入与真实分布相差过大的不可能样本，稀释有效训练信号，随机化范围的设定需要大量领域专家经验70% similar
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