Reproducing Robotic World Model Dynamics and Testing RWM-U Uncertainty
Overview
This research abstract describes an independent reimplementation of the dynamics model for the Robotic World Model (RWM) to evaluate the uncertainty estimates used by its follow-up system, RWM-U. The authors find that while ensemble disagreement effectively ranks rollout errors, the reported spread significantly understates actual error, with one-sigma coverage at 4.6% versus the expected 68% for a calibrated Gaussian. They demonstrate that rolling ensemble members on their own predictions improves coverage to 33%, and that horizon-specific rescaling restores appropriate one-sigma coverage under leave-one-episode-out evaluation. The study identifies defects in the released evaluation pipeline, noting limitations to one robot, one gait, and one dataset, and confirms that no policy was trained as part of this work.
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Event evidence and corrections
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Reported quantity · gait types: 1 other · Basis not reported
“n sampled predictions drives each member's variance toward zero. A horizon-specific rescaling restores one-sigma coverage under leave-one-episode-out evaluation, but not two-sigma coverage. We also identify two defects in the released evaluation pipeline. The study covers one robot, one gait and one dataset; it trains no policy.”
Exact source · revision 1Source owner not reported
Reported quantity · datasets: 1 other · Basis not reported
“n sampled predictions drives each member's variance toward zero. A horizon-specific rescaling restores one-sigma coverage under leave-one-episode-out evaluation, but not two-sigma coverage. We also identify two defects in the released evaluation pipeline. The study covers one robot, one gait and one dataset; it trains no policy.”
Exact source · revision 1Source owner not reported
Reported quantity · Spearman rho: 0.9 other · Basis not reported
“odes, but loses on short one-step windows. RWM-U's ensemble disagreement ranks rollout errors better than forecast step (Spearman rho = 0.90 at 100 steps), yet its spread substantially understates error: one-sigma coverage on the released checkpoint is 4.6% at 100 steps, versus 68% for a calibrated Gaussian. Rolling ensemble members on their own predictions raises coverage to 33%. The implemented squared-error loss o”
Exact source · revision 1Source owner not reported
Reported quantity · training horizon steps: 100 other · Basis not reported
“odes, but loses on short one-step windows. RWM-U's ensemble disagreement ranks rollout errors better than forecast step (Spearman rho = 0.90 at 100 steps), yet its spread substantially understates error: one-sigma coverage on the released checkpoint is 4.6% at 100 steps, versus 68% for a calibrated Gaussian. Rolling ensemble members on their own predictions raises coverage to 33%. The implemented squared-error loss o”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalRight Ranking, Wrong Scale: Reproducing the Robotic World Model's Dynamics Model and Testing the Uncertainty of Its Follow-up
This research abstract describes an independent reimplementation of the dynamics model for the Robotic World Model (RWM) to evaluate the uncertainty estimates used by its follow-up system, RWM-U. The authors find that while ensemble disagreement effectively ranks rollout errors, the reported spread significantly understates actual error, with one-sigma coverage at 4.6% versus the expected 68% for a calibrated Gaussian. They demonstrate that rolling ensemble members on their own predictions improves coverage to 33%, and that horizon-specific rescaling restores appropriate one-sigma coverage under leave-one-episode-out evaluation. The study identifies defects in the released evaluation pipeline, noting limitations to one robot, one gait, and one dataset, and confirms that no policy was trained as part of this work.
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