评估机器人模型时，必须匹配闭环操作的感知更新频率，否则结果会失真。
Do Better Imagined Rollouts Mean Better Robot Control? A Controlled Study of World-Model Evaluation Under Feedback
- 用轨迹重放模拟真实反馈控制环境，检验模型表现
- 固定预测时长下，无感知更新的滚动预测误导评估结果
- 模型评估需复现实际闭环中的感知与修正节奏
预测模型在机器人中广泛用于状态估计、规划与控制，但常以固定时长的开环预测精度衡量。本文在带有偏差里程计和间歇地标感知的差速驱动路径跟踪任务中，评估六种状态估计算法在24种传感条件下表现，使用轨迹重放、20步无测量滚动和闭环跟踪三种方式。结果显示，重放位置均方根误差与闭环横向误差相关性（0.923）高于滚动误差（0.774），且在5/24条件下选择最优估计器不同，而滚动误差则在18/24条件下误选。进一步测试不同滚动时长与感知更新间隔发现，当每步更新时，排名相关系数为0.916；无更新时下降至0.774。长预测时长若伴随定期修正仍具参考价值，而无修正的长滚动会导致与闭环行为严重偏离。对更长感知中断训练的递归估计算法，可降低联合退化场景下的误差（从1.72米降至1.06米），但在孤立中断或不同架构下效果不一致。结论：模型评估应明确预测时长与感知更新策略，离线滚动最有效时需反映闭环中的感知与修正模式。代码已开源。
原文摘要 · Abstract (English)
Predictive models are increasingly used in robotics for state estimation, planning, control, and policy evaluation, yet they are often judged by open-loop prediction accuracy over a fixed horizon. In closed-loop operation, a robot repeatedly acts, receives new measurements, updates its state estimate, and recomputes control. We study this difference in a differential-drive path-tracking task with biased odometry and intermittent landmark sensing. Six state estimators are evaluated across 24 sensing conditions using trajectory replay, a 20-step measurement-free rollout, and closed-loop tracking. Replay position RMSE correlates more strongly with closed-loop cross-track RMSE than rollout error (Spearman rho = 0.923 vs. 0.774) and selects a different estimator from the closed-loop optimum in 5/24 conditions, compared with 18/24 for the rollout metric. We then vary rollout horizon and measurement-update interval. With H=20, rank agreement decreases from rho = 0.916 with measurements at every step to rho = 0.774 with no measurements. A horizon-update grid shows that long prediction horizons remain informative when regular corrections are retained, whereas long rollouts without correction can produce rankings that differ substantially from closed-loop behavior. We also test recurrent estimators trained on longer sensing outages. This improves the EKF-anchored models under combined sensing degradation, reducing GRU-EKF cross-track RMSE from 1.72 m to 1.06 m, but the gain is not consistent across isolated outages or estimator architectures. These results show that predictive-model evaluation in robotics should specify both prediction horizon and measurement-update schedule. For models used in feedback, offline rollouts are most informative when their sensing and correction pattern reflects closed-loop operation. Code is available at https://github.com/rdharini2001/Robot_World_Model
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