🔗 All my links, tools & resources: https://ai.buildamicrosaas.com/ Stay ahead of AI with my daily newsletter, discover useful AI tools and resources, learn how to build your own software business, and connect with me across social media. _______ Robots are learning to plan with AI world models, comparing possible moves before they act. In one simulated maze experiment, success rose from 18% to 73% while using less planning computation. We explore H-JEPA’s hierarchical planning, why less detailed long-range predictions can help, and why even perfect short-range predictions don’t guarantee a successful plan. From navigating obstacles to pushing blocks, the challenge is turning a prediction into the right physical action. We also look at DeepJEPA’s approach to spending extra computation where it matters and RoboJEPA’s real robot-arm experiments in grasping, lifting and placing objects. These robotics studies show how better planning, useful representations and stronger predictors can each contribute to more capable physical AI. Subscribe to AI Tomorrow Desk for clear explanations of AI breakthroughs and what they make possible. Research and further reading: H-JEPA: https://arxiv.org/abs/2610.06805 DeepJEPA: https://arxiv.org/abs/2610.00368 RoboJEPA: https://arxiv.org/abs/2610.10515 #AI #Robotics #WorldModels