RoboJEPA：8B 参数机器人世界模型，首次给出计算量缩放定律
RoboJEPA: Scaling Robotic Latent World Models
机器人世界模型也有缩放定律了，8B 参数的 RoboJEPA 能用计算量预测真机表现，还能零样本规划长时程任务，代码全开源，搞具身的建议看看。
RoboJEPA 是基于 JEPA 架构的机器人潜在世界模型，参数规模 8B，在覆盖 12 种机器人本体的数据集上训练。研究发现其想象力误差（潜在 rollout 的误差）随计算量呈二阶幂律变化，可据此预测更大规模下的模型质量。该误差与真实机器人下游规划性能强相关，可作为真机评估的代理指标。模型可零样本部署为机器人智能体，通过朝单一目标图像规划完成长时程任务，训练与部署代码及全部 checkpoint 已开源。
RoboJEPA: Scaling Robotic Latent World Models
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.
