发表机构
University of Southern California（南加州大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究探讨机器人测试时物理诊断的有效性条件，提出六环节链条并在实验中验证，发现证据使用环节常断裂，强调评估应定位断裂点而非只看总体性能。
AI 中文摘要
当机器人面临不熟悉的物理条件时，常见的方法是收集关于什么发生了变化以及如何适应的证据。要使这种诊断能够改善行为，必须满足六个有序的经验条件：一个有意义的参考、物理条件的可识别性、所获证据的使用、决策价值、相对于固定备选方案的选择价值，以及安全的实现。我们在受控和公开环境中测试了这一链条。在我们的受控环境中，它端到端地成立。然而，在转移到未见过的机制后，它在证据使用环节断裂。在需要完整轨迹的决策上，冻结的解码器不会改变其选择。一个仅使用轨迹增量的线性模型在排除拟合的机制上恢复了正确的选择，表明轨迹包含信息但未被使用。失败集中在最丰富的证据级别：这些决策降至随机水平，而通过低成本证据解决的决策仍然正确，这种分裂被总体准确率所掩盖。在公开环境中，同一链条可能在其他环节失败。因此，成功的物理识别既不能保证证据的使用，也不能保证有用的适应；评估应确定链条在何处断裂，而不是仅依赖恢复准确率或总体性能。
英文摘要
When a robot faces unfamiliar physical conditions, a common approach is to collect evidence about what changed and adapt. For such diagnosis to improve behavior, six ordered empirical conditions must hold: a meaningful reference, identifiability of the physical condition, use of the acquired evidence, decision value, selection value over a fixed alternative, and safe realization. We test this chain in controlled and public environments. It holds end to end in our controlled environments. After transfer to unseen mechanisms, however, it breaks at evidence use. On decisions requiring the full trace, the frozen decoder does not change its choice. A linear model using only trace increments recovers the correct choice on mechanisms excluded from fitting, showing that the trace is informative but unused. The failure is concentrated at the richest evidence level: those decisions fall to chance, while decisions settled with lower-cost evidence remain correct, a split hidden by aggregate accuracy. The same chain can fail at other links in public environments. Successful physical identification therefore guarantees neither evidence use nor useful adaptation; evaluation should identify where the chain breaks rather than rely on recovery accuracy or aggregate performance alone.
Comments12 pages, 4 figures
