Roz: A Taste Test for World Model Latents
Overview
This paper presents Roz, a new evaluation framework for latent dynamics world models in robotics. The authors argue that current state-of-the-art models often fail to encode key physical properties like mass and friction, focusing instead on image similarity. Roz addresses this by defining precise encoding requirements and introducing scalable evaluation methods that can test physical properties directly. The benchmark includes 1734 individual scenarios across 10 contrastive tasks and 3 continuous tasks. Validation is performed on control tasks using the authors' own model, AnonWM. Results show that performance on Roz improves with training steps but is not necessarily correlated with reconstruction MSE. The paper advocates for adopting Roz's techniques to enable rapid iteration and fair comparison across models in the future.
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Reported quantity · scenarios: 1734 other · Basis not reported
“achieve this by defining precisely what ought to be encoded in such a world model and introducing two new evaluation methods that can directly tease physical properties and easily scale to new ones. Our evaluation, which we call Roz, includes 10 contrastive tasks and 3 continuous tasks, totaling 1734 individual scenarios. We validate our eval on control tasks. On our own model, AnonWM, we find that performance rough”
Exact source · revision 1Source owner not reported
Reported quantity · contrastive tasks: 10 other · Basis not reported
“achieve this by defining precisely what ought to be encoded in such a world model and introducing two new evaluation methods that can directly tease physical properties and easily scale to new ones. Our evaluation, which we call Roz, includes 10 contrastive tasks and 3 continuous tasks, totaling 1734 individual scenarios. We validate our eval on control tasks. On our own model, AnonWM, we find that performance rough”
Exact source · revision 1Source owner not reported
Reported quantity · continuous tasks: 3 other · Basis not reported
“achieve this by defining precisely what ought to be encoded in such a world model and introducing two new evaluation methods that can directly tease physical properties and easily scale to new ones. Our evaluation, which we call Roz, includes 10 contrastive tasks and 3 continuous tasks, totaling 1734 individual scenarios. We validate our eval on control tasks. On our own model, AnonWM, we find that performance rough”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalRoz: A Taste Test for World Model Latents
This paper presents Roz, a new evaluation framework for latent dynamics world models in robotics. The authors argue that current state-of-the-art models often fail to encode key physical properties like mass and friction, focusing instead on image similarity. Roz addresses this by defining precise encoding requirements and introducing scalable evaluation methods that can test physical properties directly. The benchmark includes 1734 individual scenarios across 10 contrastive tasks and 3 continuous tasks. Validation is performed on control tasks using the authors' own model, AnonWM. Results show that performance on Roz improves with training steps but is not necessarily correlated with reconstruction MSE. The paper advocates for adopting Roz's techniques to enable rapid iteration and fair comparison across models in the future.
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