feat: add entry from issue #33

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-14-blue)](https://pytorchkorea.github.io/Awesome-Physical-AI)
+[![Models](https://img.shields.io/badge/Models-15-blue)](https://pytorchkorea.github.io/Awesome-Physical-AI)
 [![Datasets](https://img.shields.io/badge/Datasets-11-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-30-orange)](https://pytorchkorea.github.io/Awesome-Physical-AI)
+[![Organizations](https://img.shields.io/badge/Organizations-31-orange)](https://pytorchkorea.github.io/Awesome-Physical-AI)
 [![Updated](https://img.shields.io/badge/Updated-2026-06-29-lightgrey)](https://github.com/PyTorchKorea/Awesome-Physical-AI)
 [![Dashboard](https://img.shields.io/badge/🌐_Dashboard-Live-brightgreen)](https://pytorchkorea.github.io/Awesome-Physical-AI)
 
@@ -44,6 +44,7 @@
 | [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) |
+| [RoboMamba](https://github.com/lmzpai/roboMamba) | Peking University / AI2Robotics | 2024 | manipulation | manipulator | VLA, IL | 0 | [📄](https://arxiv.org/abs/2406.04339)  |
 
 ---
 

data/models.yaml

@@ -452,3 +452,41 @@
   - VLA
   - diffusion
   - foundation-model
+- id: robomamba
+  name: RoboMamba
+  org: Peking University / AI2Robotics
+  year: 2024
+  description_en: RoboMamba is an efficient end-to-end Vision-Language-Action model
+    that leverages the Mamba state space model for robotic reasoning and manipulation
+    with linear inference complexity. It integrates a vision encoder with Mamba, aligning
+    visual tokens with language embeddings, and uses a lightweight policy head for
+    SE(3) pose prediction. RoboMamba achieves 3x faster inference than existing VLA
+    models while maintaining competitive reasoning and manipulation performance.
+  description_ko: RoboMamba는 Mamba 상태 공간 모델을 활용하여 선형 추론 복잡도로 로봇 추론과 조작을 수행하는 효율적인
+    엔드투엔드 VLA 모델입니다. 비전 인코더와 Mamba를 통합하여 시각 토큰을 언어 임베딩과 정렬하고, 경량 정책 헤드로 SE(3) 포즈 예측을
+    수행합니다. 기존 VLA 모델 대비 3배 빠른 추론 속도를 달성하면서도 경쟁력 있는 추론 및 조작 성능을 보여줍니다.
+  github_url: https://github.com/lmzpai/roboMamba
+  paper_url: https://arxiv.org/abs/2406.04339
+  hf_url: ''
+  project_url: https://sites.google.com/view/robomamba-web
+  categories:
+  - manipulation
+  hardware:
+  - manipulator
+  learning:
+  - VLA
+  - IL
+  framework:
+  - pytorch
+  communication:
+  - other
+  stats:
+    github_stars: 0
+    github_forks: 0
+    hf_downloads: 0
+    last_updated: '2026-06-29'
+  added_date: '2026-06-29'
+  tags:
+  - VLA
+  - mamba
+  - state-space-model

docs/data.json

@@ -1,10 +1,10 @@
 {
   "metadata": {
     "last_updated": "2026-06-29",
-    "total_models": 14,
+    "total_models": 15,
     "total_datasets": 11,
     "total_tools": 9,
-    "total_orgs": 30
+    "total_orgs": 31
   },
   "models": [
     {
@@ -554,6 +554,46 @@
         "diffusion",
         "foundation-model"
       ]
+    },
+    {
+      "id": "robomamba",
+      "name": "RoboMamba",
+      "org": "Peking University / AI2Robotics",
+      "year": 2024,
+      "description_en": "RoboMamba is an efficient end-to-end Vision-Language-Action model that leverages the Mamba state space model for robotic reasoning and manipulation with linear inference complexity. It integrates a vision encoder with Mamba, aligning visual tokens with language embeddings, and uses a lightweight policy head for SE(3) pose prediction. RoboMamba achieves 3x faster inference than existing VLA models while maintaining competitive reasoning and manipulation performance.",
+      "description_ko": "RoboMamba는 Mamba 상태 공간 모델을 활용하여 선형 추론 복잡도로 로봇 추론과 조작을 수행하는 효율적인 엔드투엔드 VLA 모델입니다. 비전 인코더와 Mamba를 통합하여 시각 토큰을 언어 임베딩과 정렬하고, 경량 정책 헤드로 SE(3) 포즈 예측을 수행합니다. 기존 VLA 모델 대비 3배 빠른 추론 속도를 달성하면서도 경쟁력 있는 추론 및 조작 성능을 보여줍니다.",
+      "github_url": "https://github.com/lmzpai/roboMamba",
+      "paper_url": "https://arxiv.org/abs/2406.04339",
+      "hf_url": "",
+      "project_url": "https://sites.google.com/view/robomamba-web",
+      "categories": [
+        "manipulation"
+      ],
+      "hardware": [
+        "manipulator"
+      ],
+      "learning": [
+        "VLA",
+        "IL"
+      ],
+      "framework": [
+        "pytorch"
+      ],
+      "communication": [
+        "other"
+      ],
+      "stats": {
+        "github_stars": 0,
+        "github_forks": 0,
+        "hf_downloads": 0,
+        "last_updated": "2026-06-29"
+      },
+      "added_date": "2026-06-29",
+      "tags": [
+        "VLA",
+        "mamba",
+        "state-space-model"
+      ]
     }
   ],
   "datasets": [