robotics
The Best Imagined Future Picked the Wrong Controller
Across 24 sensing conditions, a long measurement-free rollout chose a different estimator from the closed-loop optimum 18 times.
Summary
Across 24 sensing conditions, a long measurement-free rollout chose a different estimator from the closed-loop optimum 18 times.
A controlled differential-drive study compared six state estimators by replay error, a 20-step measurement-free rollout and actual closed-loop path tracking under biased odometry and intermittent landmarks. Replay position error tracked closed-loop cross-track error more closely than rollout error, with Spearman correlations of 0.923 and 0.774; replay selected a different estimator from the closed-loop optimum in 5 of 24 conditions, while the rollout metric did so in 18. Long predictions remained useful when regular measurement corrections were preserved, making the sensing and update schedule part of the evaluation—not a detail to omit.
Why it matters
Across 24 sensing conditions, a long measurement-free rollout chose a different estimator from the closed-loop optimum 18 times.
Limits and context
- Long predictions remained useful when regular measurement corrections were preserved, making the sensing and update schedule part of the evaluation—not a detail to omit.
Key claims
Across 24 sensing conditions, a long measurement-free rollout chose a different estimator from the closed-loop optimum 18 times.
Qualification: Long predictions remained useful when regular measurement corrections were preserved, making the sensing and update schedule part of the evaluation—not a detail to omit.
Evidence: source-2026-09-03-001
Sources
- arXiv preprint 2609.02811arXiv · primary research
Corrections
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