[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"tag-sim-to-real":3},{"tag":4,"articles":10},{"id":5,"name":6,"slug":6,"article_count":7,"description_zh":8,"description_en":9},"9e87f070-fefc-4f6d-9768-d8b04dc7edfb","sim-to-real",3,"Sim-to-real 指把模擬環境中的學習結果遷移到真實世界，重點在縮小物理落差與資料成本。常見做法包括域隨機化、數位孿生、擴散式軌跡生成與強化學習，特別適用於機器人操作、可變形物體與物理推理。","Sim-to-real focuses on transferring policies or models from simulation to real-world use while reducing the gap in dynamics, sensing, and control. It shows up in domain randomization, digital twins, synthetic trajectory generation, robot manipulation, and physics-based reasoning.",[11,20],{"id":12,"slug":13,"title":14,"summary":15,"category":16,"image_url":17,"cover_image":17,"language":18,"created_at":19},"8a95a2d8-eb3a-442c-b9c4-c835c79d75c5","physics-simulators-rl-llm-reasoning-en","Physics Simulators as RL Data for LLM Reasoning","Researchers train LLMs on synthetic physics from simulators and report zero-shot gains on IPhO problems, showing a new path beyond web QA data.","research","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1776146992039-q2sc.png","en","2026-04-14T06:09:33.23692+00:00",{"id":21,"slug":22,"title":23,"summary":24,"category":16,"image_url":25,"cover_image":25,"language":18,"created_at":26},"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.","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1775801212951-s38n.png","2026-04-10T06:06:35.02783+00:00"]