Scaling robust robot policies requires more than broader randomization, because physical-domain experience must remain organized and learnable throughout training. We study when a policy can benefit from harder physics and identify recoverability as a central constraint in on-policy physical-domain scaling. In on-policy training, new dynamics are useful only insofar as they remain close enough to the current policy to generate corrective on-policy data, rather than collapsing rollouts into unrecoverable failures. Using quadruped locomotion as a physically demanding benchmark for embodied generalization, we introduce HORIZON, a checkpointed frontier curriculum that expands physical domains only within the current policy's recoverable boundary. HORIZON uses rollback and boundary refinement to govern each expansion step, turning fixed randomization into a continual process of physical-domain growth. Experiments reveal three regularities of physical-domain expansion. First, direct domain widening is uneven across physical axes and often unlearnable without staged ordering. Second, domain composition is non-monotonic, and adding more domains beyond a compact core can dilute recoverable joint samples and reduce overall robustness. Third, offline distillation of isolated experts cannot substitute for the joint interaction generated by on-policy curriculum. Together, these results frame physical-domain generalization as a continual growth problem for embodied control, with recoverability as the organizing principle for on-policy expansion.

翻译：摘要：扩展鲁棒机器人策略不仅需要更广的随机化，因为物理域经验必须在整个训练过程中保持组织有序且可学习。我们研究了策略何时能从更困难的物理环境中受益，并将可恢复性确定为同策略物理域扩展的核心约束。在同策略训练中，新的动态机制仅在与当前策略足够接近时才有用，以便生成纠正性的同策略数据，而不是将轨迹坍塌为不可恢复的失败。以四足运动作为具身泛化的物理密集型基准，我们提出了HORIZON——一种检查点前沿课程，仅在当前策略的可恢复边界内扩展物理域。HORIZON通过回滚与边界细化来调控每一步扩展，将固定随机化转化为物理域持续增长的过程。实验揭示了物理域扩展的三个规律：第一，直接域扩展在物理轴上是不均匀的，且若无阶段排序则常不可学习；第二，域组合是非单调的，在紧凑核心之外添加更多域会稀释可恢复的联合样本并降低整体鲁棒性；第三，孤立专家的离线蒸馏无法替代同策略课程产生的联合交互。这些结果共同将物理域泛化定位为具身控制的持续增长问题，并以可恢复性作为同策略扩展的组织原则。