提出物理世界动态预测的可靠性问题，强调必须分离动力学检查与行为记录。
Can Predicted Dynamics Exist in the Physical World?
- 用最大聚合方法分析动作提案的可行性，发现全对位移项冗余。
- 在700个正常与5250个扰动场景中，基础过渡误差基准表现更优（AUC 0.982）。
- 适合关注机器人控制安全性和动态验证的开发者和研究者。
学习到的状态-动作提议能否在物理世界中实现？为在执行前过滤不可行指令，策略常被包裹在运行时监控器中。然而，多种诊断信号的聚合会模糊提议是违反动力学约束，还是仅偏离已有行为。本文形式化了预测-控制接口，并证明在最大聚合复合体中，全对位移项是冗余的。我们在700个正常和5,250个合成扰动的32次转移推送窗口（PushT）上评估了这些监控器，仅监控平面推杆位置和目标。一个简单的过渡-RMSE基线（AUC 0.982）优于异构最大聚合监控器（AUC 0.957）。结论表明，物理过渡检查必须严格区分于经验日志。
原文摘要 · Abstract (English)
Can learned state-action proposals exist in the physical world? To filter infeasible commands before execution, policies are often wrapped in a runtime monitor. However, aggregating diverse diagnostic signals obscures whether a proposal violates dynamic transitions or merely departs from recorded behavior. We formalize this prediction-control interface and prove that the all-pairs displacement term is redundant within a max-aggregated composite. We evaluate these monitors on 700 nominal and 5,250 synthetically perturbed 32-transition PushT windows, monitoring only planar pusher positions and goals. A simple transition-RMSE baseline (AUC 0.982) outperforms a heterogeneous max-aggregated monitor (AUC 0.957). We conclude that physical transition checks must be strictly separated from empirical logs.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。