feat: add entry from issue #19

Author: github-actions[bot]

README.md

@@ -3,10 +3,10 @@
 > 🤖 Physical AI (Robotics & Embodied AI) 분야의 오픈소스 모델, 데이터셋, 시뮬레이터를 체계적으로 정리한 큐레이션 리스트.
 > A curated list of open-source models, datasets, and simulators for Physical AI (Robotics & Embodied AI).
 
-[![Models](https://img.shields.io/badge/Models-13-blue)](https://pytorchkorea.github.io/Awesome-Physical-AI)
+[![Models](https://img.shields.io/badge/Models-14-blue)](https://pytorchkorea.github.io/Awesome-Physical-AI)
 [![Datasets](https://img.shields.io/badge/Datasets-10-green)](https://pytorchkorea.github.io/Awesome-Physical-AI)
 [![Simulators](https://img.shields.io/badge/Simulators-9-purple)](https://pytorchkorea.github.io/Awesome-Physical-AI)
-[![Organizations](https://img.shields.io/badge/Organizations-28-orange)](https://pytorchkorea.github.io/Awesome-Physical-AI)
+[![Organizations](https://img.shields.io/badge/Organizations-29-orange)](https://pytorchkorea.github.io/Awesome-Physical-AI)
 [![Updated](https://img.shields.io/badge/Updated-2026-06-21-lightgrey)](https://github.com/PyTorchKorea/Awesome-Physical-AI)
 [![Dashboard](https://img.shields.io/badge/🌐_Dashboard-Live-brightgreen)](https://pytorchkorea.github.io/Awesome-Physical-AI)
 
@@ -43,6 +43,7 @@
 | [RoboFlamingo](https://github.com/RoboFlamingo/RoboFlamingo) | ByteDance | 2023 | manipulation | manipulator | VLA | 434 | [📄](https://arxiv.org/abs/2311.01378)  |
 | [GR-1](https://github.com/bytedance/GR-1) | BAAI / Beijing Academy of AI | 2024 | manipulation | manipulator | VLA, IL | 312 | [📄](https://arxiv.org/abs/2312.13139)  |
 | [CrossFormer](https://github.com/rail-berkeley/crossformer) | UC Berkeley / others | 2024 | manipulation | manipulator, mobile | IL, VLA | 283 | [📄](https://arxiv.org/abs/2408.11812) [🤗](https://huggingface.co/rail-berkeley/crossformer) |
+| [CogACT](https://github.com/microsoft/CogACT) | Tsinghua University / Microsoft Research Asia | 2024 | manipulation | manipulator | VLA, diffusion | 0 | [📄](https://cogact.github.io/CogACT_paper.pdf) [🤗](https://huggingface.co/CogACT) |
 
 ---
 

data/models.yaml

@@ -416,3 +416,39 @@
     last_updated: '2026-06-14'
   added_date: '2026-05-17'
   tags: []
+- id: cogact
+  name: CogACT
+  org: Tsinghua University / Microsoft Research Asia
+  year: 2024
+  description_en: CogACT is a componentized Vision-Language-Action architecture that
+    decouples cognition from action. It utilizes powerful Vision-Language Models  to
+    extract cognitive features, which then condition a specialized Diffusion Transformer-based
+    action module to predict continuous, temporally-correlated robotic action sequences.
+  description_ko: CogACT는 기존의 단일 신경망 모델들과 달리 인지와 행동을 명확히 분리한 컴포넌트형 비전-언어-행동 모델입니다.
+    강력한 비전-언어 모델을 통해 인지적 특징을 추출하고, 이를 조건으로 특화된 Diffusion Transformer 기반의 행동 모델이 복잡하고
+    연속적인 로봇의 물리적 제어 궤적을 예측합니다.
+  github_url: https://github.com/microsoft/CogACT
+  paper_url: https://cogact.github.io/CogACT_paper.pdf
+  hf_url: https://huggingface.co/CogACT
+  project_url: https://cogact.github.io/
+  categories:
+  - manipulation
+  hardware:
+  - manipulator
+  learning:
+  - VLA
+  - diffusion
+  framework:
+  - pytorch
+  communication:
+  - other
+  stats:
+    github_stars: 0
+    github_forks: 0
+    hf_downloads: 0
+    last_updated: '2026-06-21'
+  added_date: '2026-06-21'
+  tags:
+  - VLA
+  - diffusion
+  - foundation-model

docs/data.json

@@ -1,10 +1,10 @@
 {
   "metadata": {
     "last_updated": "2026-06-21",
-    "total_models": 13,
+    "total_models": 14,
     "total_datasets": 10,
     "total_tools": 9,
-    "total_orgs": 28
+    "total_orgs": 29
   },
   "models": [
     {
@@ -514,6 +514,46 @@
       },
       "added_date": "2026-05-17",
       "tags": []
+    },
+    {
+      "id": "cogact",
+      "name": "CogACT",
+      "org": "Tsinghua University / Microsoft Research Asia",
+      "year": 2024,
+      "description_en": "CogACT is a componentized Vision-Language-Action architecture that decouples cognition from action. It utilizes powerful Vision-Language Models  to extract cognitive features, which then condition a specialized Diffusion Transformer-based action module to predict continuous, temporally-correlated robotic action sequences.",
+      "description_ko": "CogACT는 기존의 단일 신경망 모델들과 달리 인지와 행동을 명확히 분리한 컴포넌트형 비전-언어-행동 모델입니다. 강력한 비전-언어 모델을 통해 인지적 특징을 추출하고, 이를 조건으로 특화된 Diffusion Transformer 기반의 행동 모델이 복잡하고 연속적인 로봇의 물리적 제어 궤적을 예측합니다.",
+      "github_url": "https://github.com/microsoft/CogACT",
+      "paper_url": "https://cogact.github.io/CogACT_paper.pdf",
+      "hf_url": "https://huggingface.co/CogACT",
+      "project_url": "https://cogact.github.io/",
+      "categories": [
+        "manipulation"
+      ],
+      "hardware": [
+        "manipulator"
+      ],
+      "learning": [
+        "VLA",
+        "diffusion"
+      ],
+      "framework": [
+        "pytorch"
+      ],
+      "communication": [
+        "other"
+      ],
+      "stats": {
+        "github_stars": 0,
+        "github_forks": 0,
+        "hf_downloads": 0,
+        "last_updated": "2026-06-21"
+      },
+      "added_date": "2026-06-21",
+      "tags": [
+        "VLA",
+        "diffusion",
+        "foundation-model"
+      ]
     }
   ],
   "datasets": [