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Life sciences · Preprint
arXiv · September 3, 2026
Early or partial results. Treat as a signal, not a conclusion.
This is an unreviewed preprint describing a JEPA-based robotic world model augmented with inverse dynamics and state alignment. The method achieves high success rates on four simulation benchmarks (TwoRoom 100%, PushT 98%, OGBench-Cube 87%, Reacher comparable to baseline), and ablation evidence suggests state alignment improves planning over inverse dynamics alone. However, the work is limited to simulation, lacks statistical rigor, and has not undergone peer review.
Algorithmic comparison with ablation study on simulation benchmarks. Intervention: End-to-end JEPA world model augmented with inverse dynamics and state alignment. Compared with: LeWorldModel baseline and inverse dynamics (IDM) alone.
TwoRoom task: model attains 100% success rate PushT task: model attains 98% success rate OGBench-Cube task: model attains 87% success rate
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Unreviewed technical report on a robotic learning method, demonstrating improved performance on four simulation benchmarks without clinical or real-world validation.
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Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
Action-conditioned JEPA world models enable planning toward visually specified goals without reconstructing future pixels, yet latent prediction alone does not explicitly encourage the learned representations to retain information relevant to robotic control. We introduce an end-to-end JEPA world model that augments latent prediction with inverse dynamics (IDM) and state alignment (SA). While inverse dynamics discourages latent collapse and makes latent transitions informative of the actions that produced them, state alignment grounds consecutive representations in their associated physical configuration and motion. Across four benchmark tasks, our model attains the highest success rates on TwoRoom (100%), PushT (98%), and OGBench-Cube (87%), while performing comparably to LeWorldModel on Reacher. Our ablation further shows that adding state alignment consistently improves planning success over IDM alone across all four tasks. Although LeWorldModel, our primary baseline, attains higher average straightening on OGBench-Cube, transition-subspace analysis shows that its transition energy is concentrated in a substantially lower-dimensional subspace. Our state-aligned model exhibits a higher effective transition dimension than LeWorldModel and improves planning over IDM alone, supporting state alignment as an effective complement to inverse dynamics for robotic planning.
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