[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"tag-robotics":3},{"tag":4,"articles":10},{"id":5,"name":6,"slug":6,"article_count":7,"description_zh":8,"description_en":9},"bbb7896a-2a77-418e-b199-5614fb32b3d5","robotics",3,"機器人技術涵蓋感知、控制、模擬與規劃，重點在讓模型能在真實環境中穩定行動。這個標籤聚焦世界模型、階層式規劃、少樣本示範與可變形物體模擬等主題，反映如何降低算力成本並提升長時序控制與零樣本遷移能力。","Robotics here focuses on the methods that let machines act reliably in the physical world: perception, control, simulation, and planning. Topics include world models, hierarchical planning, sparse-demonstration learning, and deformable-object simulation, all aimed at better long-horizon control with less compute.",[11,20],{"id":12,"slug":13,"title":14,"summary":15,"category":16,"image_url":17,"cover_image":17,"language":18,"created_at":19},"f247a589-9cfb-4ff5-8857-d9bb49454977","sim1-physics-aligned-deformable-worlds-en","SIM1 turns sparse demos into deformable-world data","SIM1 grounds deformable-object simulation in real scenes, then scales sparse demos into synthetic training data for data-efficient robot policy learning.","research","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1775801212951-s38n.png","en","2026-04-10T06:06:35.02783+00:00",{"id":21,"slug":22,"title":23,"summary":24,"category":16,"image_url":25,"cover_image":25,"language":18,"created_at":26},"5e1d7109-dea2-4002-8e74-bf6331f46c05","hierarchical-planning-latent-world-models-en","Hierarchical Planning Cuts World-Model Search Cost","A hierarchical latent world-model planner improves long-horizon control and cuts planning compute, with zero-shot gains on real robots.","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1775455787999-4nzg.png","2026-04-06T06:09:31.947292+00:00"]