Archived
2026-10-08RoboJEPA: Scaling Robotic Latent World Models
Artem Zholus, Nicolas Beltran-Velez, Jianhao Yuan, Sarath Chandar, Tushar Nagarajan, Daniel Severo, et al.
arXiv:2610.10515v1Today’s standout paper is RoboJEPA, which asks a practical question for robotics: how do latent world models improve as we scale them up? These models learn compressed representations of the world and predict what will happen next, which can support planning without simulating every pixel. The challenge is that robot data is expensive, so researchers need to know whether bigger models and more compute really buy better behavior. RoboJEPA is trained on a large dataset spanning 12 robot embodiments, and the authors find that its imagination error follows a clean power law with compute. Even more importantly, that latent prediction error tracks downstream planning success, so it can act as a proxy for real-robot performance. The model also works zero-shot as an agent, planning toward a goal image on real hardware. Why it matters: this gives robotics a scaling law, a way to forecast progress and allocate compute more intelligently, much like language models benefited from scaling predictions.
Also spotted that day
Never Look Back: Understanding Persistence in 3D Object Memory from Egocentric VideosDecoupling Exploration from Optimization in RLVREngramEdit: Decoupled Knowledge Updates in LLMs through Conditional MemoryLong-WAM: Scaling the Context of World-Action ModelsDecentralized SGD under Heavy-Tailed Noise: Optimal Convergence Rates and the Role of Gradient ClippingRephrase Before You Act: Characterizing and Mitigating Language Sensitivity in Vision-Language-Action ModelsDistilling Graph Geometry: Knowledge Gap from GNNs to MLPsWhy Forget-Only Unlearning Needs MemorizationSciExam for ENSO: Can AI Agents Build Climate Models?Your Prompt Should Do More: Effects of Retrieval Instructions in Embedding ModelsRECAST: Learning to Compute the Right Context through Adaptive Evidence RoutingValidity Without Ground Truth: What Stated-Preference Economics Offers the Evaluation of Language ModelsOracle-Efficient and Parameter-Free Agnostic Smoothed Online LearningEmbodiedRSI: Active Continual Robot Learning Through Hypothesis-Guided Co-Evolution
Previous daily papers
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