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机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
TierCEM将严格约束优先级直接融入交叉熵精英选择，无需权重，在导航与推动任务中验证了按优先级处理冲突约束的有效性。
AI 中文摘要
当约束相互冲突时，优化器必须决定保留哪些要求、放宽哪些要求。一方面，优先级排序指定了哪些要求优先；另一方面，基于惩罚的公式通过数值权重来编码其相对重要性。根据这些权重，一个解可以在违反预期优先级的同时提高其加权得分。我们提出了TierCEM，这是交叉熵方法的一种变体，它将严格的约束优先级直接纳入精英选择中，而无需为每个约束设置重要性权重。TierCEM的工作原理是按从最高优先级到最低优先级的顺序对采样候选进行逐级过滤。如果某个约束消除了所有剩余候选，TierCEM将回退到最后一个非空集合，并选择对该阻塞约束违反最小的精英，递归地保持所有更高优先级约束的满足。我们在本体感觉和学习的世界模型设置中，对2D导航和接触丰富的推动任务评估了TierCEM。实验表明，反转约束顺序会改变冲突下违反的约束。对任务目标进展的优先排序还使TierCEM能够在低优先级约束阻碍进一步进展时放宽它们。
英文摘要
When constraints conflict, an optimizer must determine which requirements to preserve and which to relax. On the one hand, a priority ordering specifies which requirements take precedence. On the other hand, penalty-based formulations encode their relative importance through numerical weights. Depending on these weights, a solution can improve its weighted score while violating intended priorities. We introduce TierCEM, a variant of the cross-entropy method that incorporates strict constraint priorities directly into elite selection without requiring per-constraint importance weights. TierCEM works by sequentially filtering sampled candidates, from highest- to lowest-priority constraint. If and when a constraint eliminates all remaining candidates, TierCEM returns to the last nonempty set and selects elites with the smallest violations of that blocking constraint, recursively preserving satisfaction of all higher-priority constraints. We evaluate TierCEM on 2D navigation and contact-rich pushing tasks in proprioceptive and learned world-model settings. Experiments show that reversing the constraint ordering changes which constraints are violated under conflict. Prioritizing progress toward the task objective also enables TierCEM to relax lower-priority constraints when they would otherwise prevent further progress.
