RoboJEPA: Scaling Robotic Latent World Models

Hugging Face Daily Papers Papers
world-model jepa robotics scaling-laws embodied-ai latent-representation

Summary

RoboJEPA 基于 JEPA 架构，在涵盖 12 种机器人具身的大型真实机器人数据集上训练，首次为多具身机器人世界模型建立了 scaling law：其 latent rollout 的“想象力误差”随计算量呈二阶幂律变化，并可作为真实机器人规划性能的可靠代理。该模型参数量达 8B，是迄今最大的 JEPA 预测模型，支持零样本部署为长时程规划的机器人智能体，并开源全部检查点与训练/部署代码。

Latent world models have shown a remarkable ability to predict future states and to plan in the real world. In practice, however, we lack a principled way to estimate how their capabilities scale with model size, data, and compute, an open problem that slows progress in the field. In this work we present RoboJEPA, a world model based on the Joint Embedding Predictive Architecture (JEPA) and trained on a large-scale dataset spanning 12 robotic embodiments. We show that RoboJEPA's imagination error, the error of its latent rollouts, follows a second-order power law in compute, allowing us to predict model quality well beyond the scale at which the law is fit. We further show that downstream robotic planning performance improves predictably with compute, and that imagination error is strongly correlated with it, making it a reliable proxy for real-robot evaluation. Finally, we demonstrate that latent world models can be deployed zero-shot as robotic agents, planning toward a single goal image to solve tasks requiring long-horizon planning on real hardware. We release all model checkpoints together with our training and robot deployment code. To our knowledge, this is the first work to establish scaling laws for multi-embodiment robotic world models trained on real robot data, and RoboJEPA, at 8B parameters, is the largest JEPA predictor model trained to date.
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Abstract

Latentworldmodelshaveshownaremarkableabilitytopredictfuturestatesandtoplanintherealworld.Inpractice,however,welackaprincipledwaytoestimatehowtheircapabilitiesscalewithmodelsize,data,andcompute,anopenproblemthatslowsprogressinthefield.InthisworkwepresentRoboJEPA,aworldmodelbasedontheJointEmbeddingPredictiveArchitecture(JEPA)andtrainedonalarge-scaledatasetspanning12roboticembodiments.WeshowthatRoboJEPA’simaginationerror,theerrorofitslatentrollouts,followsasecond-orderpowerlawincompute,allowingustopredictmodelqualitywellbeyondthescaleatwhichthelawisfit.Wefurthershowthatdownstreamroboticplanningperformanceimprovespredictablywithcompute,andthatimaginationerrorisstronglycorrelatedwithit,makingitareliableproxyforreal-robotevaluation.Finally,wedemonstratethatlatentworldmodelscanbedeployedzero-shotasroboticagents,planningtowardasinglegoalimagetosolvetasksrequiringlong-horizonplanningonrealhardware.Wereleaseallmodelcheckpointstogetherwithourtrainingandrobotdeploymentcode.Toourknowledge,thisisthefirstworktoestablishscalinglawsformulti-embodimentroboticworldmodelstrainedonrealrobotdata,andRoboJEPA,at8Bparameters,isthelargestJEPApredictormodeltrainedtodate.

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