Detecting Silent Action-Interface Mismatch with Calibrated Relational World Models
Overview
This research paper introduces a method to detect silent action-interface mismatches in robotics by using learned state-action-effect relations as offline contracts. The authors evaluate their approach on ten LIBERO Spatial tasks, demonstrating that a four-component joint Gaussian mixture model achieves a 65.7% recall rate with a 95.8% localization score under a 2.5% false-positive rate. The study highlights the tradeoff between detection and localization and notes that earlier results on Object/Goal tasks do not extend to other benchmarks like DROID or HYDRA, where performance fails specific gates.
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Event evidence and corrections
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Reported quantity · source episodes: 432 other · reported trials
“ulary supports closed-set localization. On ten LIBERO Spatial tasks comprising 432 source episodes, a capacity-controlled comparison at 2.5% empirical clean false-positive rate gives 46.1% recall for a ridge forward residual, 61.0% for a four-component joint Gaussian mixture, and 65.7% for the exact conditional of that same mixture. The joint score retains a 14.9 percentage-point advantage over ridge, while condition”
Exact source · revision 1Source owner not reported
Reported quantity · Reported recall: 46.1 percent · Basis not reported
“ulary supports closed-set localization. On ten LIBERO Spatial tasks comprising 432 source episodes, a capacity-controlled comparison at 2.5% empirical clean false-positive rate gives 46.1% recall for a ridge forward residual, 61.0% for a four-component joint Gaussian mixture, and 65.7% for the exact conditional of that same mixture. The joint score retains a 14.9 percentage-point advantage over ridge, while condition”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalDetecting Silent Action-Interface Mismatch with Calibrated Relational World Models
This research paper introduces a method to detect silent action-interface mismatches in robotics by using learned state-action-effect relations as offline contracts. The authors evaluate their approach on ten LIBERO Spatial tasks, demonstrating that a four-component joint Gaussian mixture model achieves a 65.7% recall rate with a 95.8% localization score under a 2.5% false-positive rate. The study highlights the tradeoff between detection and localization and notes that earlier results on Object/Goal tasks do not extend to other benchmarks like DROID or HYDRA, where performance fails specific gates.
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