From bf7d32dd6c64ae050e3fe71f9c1b64e177d53754 Mon Sep 17 00:00:00 2001
From: "github-actions[bot]" <github-actions[bot]@users.noreply.github.com>
Date: Mon, 27 Apr 2026 14:18:13 +0000
Subject: [PATCH] feat: add entry from issue #3

---
 README.md        | 12 ++++++------
 data/models.yaml | 27 +++++++++++++++++++++++++++
 docs/data.json   | 39 ++++++++++++++++++++++++++++++++++++---
 docs/index.html  |  2 +-
 4 files changed, 70 insertions(+), 10 deletions(-)

diff --git a/README.md b/README.md
index 1fcbf4f..f59f196 100644
--- a/README.md
+++ b/README.md
@@ -3,11 +3,11 @@
 > 🤖 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-12-blue)](https://pytorchkorea.github.io/Awesome-Physical-AI)
+[![Models](https://img.shields.io/badge/Models-13-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-27-orange)](https://pytorchkorea.github.io/Awesome-Physical-AI)
-[![Updated](https://img.shields.io/badge/Updated-2026-04-25-lightgrey)](https://github.com/PyTorchKorea/Awesome-Physical-AI)
+[![Organizations](https://img.shields.io/badge/Organizations-28-orange)](https://pytorchkorea.github.io/Awesome-Physical-AI)
+[![Updated](https://img.shields.io/badge/Updated-2026-04-27-lightgrey)](https://github.com/PyTorchKorea/Awesome-Physical-AI)
 [![Dashboard](https://img.shields.io/badge/🌐_Dashboard-Live-brightgreen)](https://pytorchkorea.github.io/Awesome-Physical-AI)
 
 > **[👉 인터랙티브 대시보드에서 필터링 및 시각화 보기 | View Interactive Dashboard](https://pytorchkorea.github.io/Awesome-Physical-AI)**
@@ -42,6 +42,7 @@
 | [RoboFlamingo](https://github.com/RoboFlamingo/RoboFlamingo) | ByteDance | 2023 | manipulation | manipulator | VLA | 429 | [📄](https://arxiv.org/abs/2311.01378)  |
 | [GR-1](https://github.com/bytedance/GR-1) | BAAI / Beijing Academy of AI | 2024 | manipulation | manipulator | VLA, IL | 309 | [📄](https://arxiv.org/abs/2312.13139)  |
 | [CrossFormer](https://github.com/rail-berkeley/crossformer) | UC Berkeley / others | 2024 | manipulation | manipulator, mobile | IL, VLA | 282 | [📄](https://arxiv.org/abs/2408.11812) [🤗](https://huggingface.co/rail-berkeley/crossformer) |
+| [ㅇㄹㄴ](ㄴㄹㄴ) | ㅇㄴㄹ | 1212 | navigation, dexterous | biped, mobile | VLA | 0 | [📄](ㄴㄹㅇ) [🤗](ㄴㅇㄹ) |
 
 ---
 
@@ -84,12 +85,11 @@
 
 ## 🤝 How to Contribute
 
-새 항목을 추가하려면 GitHub Issue를 열어주세요.
-To add a new entry, please open a GitHub Issue:
+새 모델 또는 데이터셋을 추가하려면 GitHub Issue를 열어주세요.
+To add a new model or dataset, please open a GitHub Issue:
 
 - **[➕ Add a Model](https://github.com/PyTorchKorea/Awesome-Physical-AI/issues/new?template=add-model.yml)**
 - **[➕ Add a Dataset](https://github.com/PyTorchKorea/Awesome-Physical-AI/issues/new?template=add-dataset.yml)**
-- **[➕ Add a Simulator](https://github.com/PyTorchKorea/Awesome-Physical-AI/issues/new?template=add-simulator.yml)**
 
 이슈가 등록되면 봇이 자동으로 PR을 생성하고, 관리자가 검토 후 머지합니다.
 A bot will automatically create a PR from your issue for admin review.
diff --git a/data/models.yaml b/data/models.yaml
index 004d38c..f72155c 100644
--- a/data/models.yaml
+++ b/data/models.yaml
@@ -386,3 +386,30 @@
   - cross-embodiment
   - generalist
   - transformer
+- id: custom
+  name: ㅇㄹㄴ
+  org: ㅇㄴㄹ
+  year: 1212
+  description_en: ㅇㄹㄴㄷㄹㄴㄹㅇㄴㄹㅎ
+  description_ko: ''
+  github_url: ㄴㄹㄴ
+  paper_url: ㄴㄹㅇ
+  hf_url: ㄴㅇㄹ
+  project_url: ㅇㄴㄹㄹ
+  categories:
+  - navigation
+  - dexterous
+  hardware:
+  - biped
+  - mobile
+  learning:
+  - VLA
+  framework: []
+  communication: []
+  stats:
+    github_stars: 0
+    github_forks: 0
+    hf_downloads: 0
+    last_updated: '2026-04-27'
+  added_date: '2026-04-27'
+  tags: []
diff --git a/docs/data.json b/docs/data.json
index 64aeca8..e4da4a0 100644
--- a/docs/data.json
+++ b/docs/data.json
@@ -1,10 +1,10 @@
 {
   "metadata": {
-    "last_updated": "2026-04-25",
-    "total_models": 12,
+    "last_updated": "2026-04-27",
+    "total_models": 13,
     "total_datasets": 10,
     "total_tools": 9,
-    "total_orgs": 27
+    "total_orgs": 28
   },
   "models": [
     {
@@ -480,6 +480,39 @@
         "generalist",
         "transformer"
       ]
+    },
+    {
+      "id": "custom",
+      "name": "ㅇㄹㄴ",
+      "org": "ㅇㄴㄹ",
+      "year": 1212,
+      "description_en": "ㅇㄹㄴㄷㄹㄴㄹㅇㄴㄹㅎ",
+      "description_ko": "",
+      "github_url": "ㄴㄹㄴ",
+      "paper_url": "ㄴㄹㅇ",
+      "hf_url": "ㄴㅇㄹ",
+      "project_url": "ㅇㄴㄹㄹ",
+      "categories": [
+        "navigation",
+        "dexterous"
+      ],
+      "hardware": [
+        "biped",
+        "mobile"
+      ],
+      "learning": [
+        "VLA"
+      ],
+      "framework": [],
+      "communication": [],
+      "stats": {
+        "github_stars": 0,
+        "github_forks": 0,
+        "hf_downloads": 0,
+        "last_updated": "2026-04-27"
+      },
+      "added_date": "2026-04-27",
+      "tags": []
     }
   ],
   "datasets": [
diff --git a/docs/index.html b/docs/index.html
index 6de504e..4f5ecbb 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -28,7 +28,7 @@
   <script src="https://cdn.jsdelivr.net/npm/chart.js@4.4.0/dist/chart.umd.min.js"></script>
 
   <!-- Embedded data — auto-replaced by scripts/generate_site.py -->
-  <script id="embedded-data">window.__PHYSICAL_AI_DATA__ = {"metadata": {"last_updated": "2026-04-25", "total_models": 12, "total_datasets": 10, "total_tools": 9, "total_orgs": 27}, "models": [{"id": "lerobot", "name": "LeRobot", "org": "Hugging Face", "year": 2024, "description_en": "End-to-end learning for real-world robotics. Provides datasets, pretrained models, and training code for manipulation tasks.", "description_ko": "실제 로봇을 위한 엔드-투-엔드 학습 프레임워크. 조작 태스크를 위한 데이터셋, 사전학습 모델, 학습 코드를 제공합니다.", "github_url": "https://github.com/huggingface/lerobot", "paper_url": "https://arxiv.org/abs/2408.01730", "hf_url": "https://huggingface.co/lerobot", "project_url": "", "categories": ["manipulation"], "hardware": ["manipulator"], "learning": ["IL", "diffusion", "VLA"], "framework": ["pytorch"], "communication": [], "stats": {"github_stars": 23337, "github_forks": 4280, "hf_downloads": 0, "last_updated": "2026-04-19"}, "added_date": "2024-06-01", "tags": ["imitation-learning", "diffusion-policy", "act"]}, {"id": "pi0", "name": "π0 (pi-zero)", "org": "Physical Intelligence", "year": 2024, "description_en": "A generalist robot foundation model based on a flow-matching policy. Supports dexterous manipulation and whole-body control.", "description_ko": "플로우 매칭 정책 기반의 범용 로봇 파운데이션 모델. 정교한 조작과 전신 제어를 지원합니다.", "github_url": "https://github.com/Physical-Intelligence/openpi", "paper_url": "https://arxiv.org/abs/2410.24164", "hf_url": "https://huggingface.co/physical-intelligence/pi0", "project_url": "https://www.physicalintelligence.company/blog/pi0", "categories": ["manipulation", "whole-body"], "hardware": ["manipulator", "humanoid"], "learning": ["VLA", "diffusion"], "framework": ["jax", "pytorch"], "communication": [], "stats": {"github_stars": 11365, "github_forks": 1782, "hf_downloads": 0, "last_updated": "2026-04-19"}, "added_date": "2024-10-31", "tags": ["foundation-model", "flow-matching", "dexterous", "open-weights"]}, {"id": "openvla", "name": "OpenVLA", "org": "Stanford / UC Berkeley", "year": 2024, "description_en": "An open-source Vision-Language-Action model fine-tuned from Prismatic VLM on the Open X-Embodiment dataset.", "description_ko": "Open X-Embodiment 데이터셋으로 Prismatic VLM을 파인튜닝한 오픈소스 Vision-Language-Action 모델.", "github_url": "https://github.com/openvla/openvla", "paper_url": "https://arxiv.org/abs/2406.09246", "hf_url": "https://huggingface.co/openvla/openvla-7b", "project_url": "https://openvla.github.io", "categories": ["manipulation"], "hardware": ["manipulator"], "learning": ["VLA"], "framework": ["pytorch"], "communication": [], "stats": {"github_stars": 5936, "github_forks": 701, "hf_downloads": 1709553, "last_updated": "2026-04-19"}, "added_date": "2024-06-13", "tags": ["VLA", "7B", "prismatic"]}, {"id": "octo", "name": "Octo", "org": "UC Berkeley / Stanford / CMU / others", "year": 2023, "description_en": "A transformer-based robot policy pretrained on 800k+ robot trajectories. Supports diverse robot morphologies via tokenized observations.", "description_ko": "80만 개 이상의 로봇 궤적으로 사전학습된 트랜스포머 기반 로봇 정책. 토큰화된 관측을 통해 다양한 로봇 형태를 지원합니다.", "github_url": "https://github.com/octo-models/octo", "paper_url": "https://arxiv.org/abs/2405.12213", "hf_url": "https://huggingface.co/rail-berkeley/octo-base", "project_url": "https://octo-models.github.io", "categories": ["manipulation"], "hardware": ["manipulator", "mobile"], "learning": ["IL", "VLA"], "framework": ["jax"], "communication": [], "stats": {"github_stars": 1623, "github_forks": 269, "hf_downloads": 71, "last_updated": "2026-04-19"}, "added_date": "2023-10-01", "tags": ["generalist", "transformer", "multi-robot"]}, {"id": "act", "name": "ACT (Action Chunking with Transformers)", "org": "Stanford", "year": 2023, "description_en": "Imitation learning with action chunking. Achieves dexterous bimanual manipulation from 50 demonstrations using a CVAE-based transformer.", "description_ko": "액션 청킹을 활용한 모방 학습. CVAE 기반 트랜스포머로 50개의 시연만으로 정교한 양팔 조작을 달성합니다.", "github_url": "https://github.com/tonyzhaozh/act", "paper_url": "https://arxiv.org/abs/2304.13705", "hf_url": "", "project_url": "https://tonyzhaozh.github.io/aloha/", "categories": ["manipulation", "dexterous"], "hardware": ["manipulator"], "learning": ["IL"], "framework": ["pytorch"], "communication": ["ros2"], "stats": {"github_stars": 1879, "github_forks": 372, "hf_downloads": 0, "last_updated": "2026-04-19"}, "added_date": "2023-04-01", "tags": ["ALOHA", "bimanual", "CVAE", "imitation-learning"]}, {"id": "diffusion-policy", "name": "Diffusion Policy", "org": "Columbia University", "year": 2023, "description_en": "Visuomotor policy learning via conditional denoising diffusion. Achieves state-of-the-art performance on 11 robotic manipulation tasks.", "description_ko": "조건부 노이즈 제거 확산을 통한 시각운동 정책 학습. 11개의 로봇 조작 태스크에서 최고 성능을 달성합니다.", "github_url": "https://github.com/real-stanford/diffusion_policy", "paper_url": "https://arxiv.org/abs/2303.04137", "hf_url": "", "project_url": "https://diffusion-policy.cs.columbia.edu", "categories": ["manipulation"], "hardware": ["manipulator"], "learning": ["IL", "diffusion"], "framework": ["pytorch"], "communication": [], "stats": {"github_stars": 4026, "github_forks": 742, "hf_downloads": 0, "last_updated": "2026-04-19"}, "added_date": "2023-03-07", "tags": ["diffusion", "visuomotor", "score-matching"]}, {"id": "mobile-aloha", "name": "Mobile ALOHA", "org": "Stanford", "year": 2024, "description_en": "Whole-body teleoperation and imitation learning for mobile manipulation. Extends ALOHA with a mobile base for household tasks.", "description_ko": "모바일 조작을 위한 전신 원격 조작 및 모방 학습. ALOHA를 모바일 베이스로 확장하여 가정용 태스크를 수행합니다.", "github_url": "https://github.com/MarkFzp/mobile-aloha", "paper_url": "https://arxiv.org/abs/2401.02117", "hf_url": "", "project_url": "https://mobile-aloha.github.io", "categories": ["manipulation", "navigation", "whole-body"], "hardware": ["mobile", "manipulator"], "learning": ["IL"], "framework": ["pytorch"], "communication": ["ros2"], "stats": {"github_stars": 4407, "github_forks": 733, "hf_downloads": 0, "last_updated": "2026-04-19"}, "added_date": "2024-01-04", "tags": ["mobile-manipulation", "household", "teleoperation"]}, {"id": "humanplus", "name": "HumanPlus", "org": "Stanford", "year": 2024, "description_en": "Humanoid shadowing and imitation from human video. A humanoid robot that learns to shadow humans and imitate skills from egocentric video.", "description_ko": "인간 영상으로부터의 휴머노이드 섀도잉 및 모방 학습. 자아중심 비디오에서 기술을 모방하는 휴머노이드 로봇입니다.", "github_url": "https://github.com/MarkFzp/humanplus", "paper_url": "https://arxiv.org/abs/2406.10454", "hf_url": "", "project_url": "https://humanoid-ai.github.io", "categories": ["manipulation", "whole-body"], "hardware": ["humanoid"], "learning": ["IL"], "framework": ["pytorch"], "communication": ["ros2"], "stats": {"github_stars": 837, "github_forks": 123, "hf_downloads": 0, "last_updated": "2026-04-19"}, "added_date": "2024-06-14", "tags": ["humanoid", "shadowing", "egocentric"]}, {"id": "groot", "name": "GR00T N1", "org": "NVIDIA", "year": 2025, "description_en": "NVIDIA's humanoid foundation model with cross-embodiment generalization. Combines vision-language understanding with action generation.", "description_ko": "NVIDIA의 교차 구현 일반화 기능을 갖춘 휴머노이드 파운데이션 모델. 비전-언어 이해와 행동 생성을 결합합니다.", "github_url": "https://github.com/NVIDIA/Isaac-GR00T", "paper_url": "https://arxiv.org/abs/2503.14734", "hf_url": "https://huggingface.co/nvidia/GR00T-N1-2B", "project_url": "https://developer.nvidia.com/isaac/gr00t", "categories": ["manipulation", "whole-body"], "hardware": ["humanoid"], "learning": ["VLA", "IL"], "framework": ["pytorch"], "communication": ["ros2"], "stats": {"github_stars": 6729, "github_forks": 1137, "hf_downloads": 283, "last_updated": "2026-04-19"}, "added_date": "2025-03-19", "tags": ["humanoid", "foundation-model", "NVIDIA", "cross-embodiment"]}, {"id": "roboflamingo", "name": "RoboFlamingo", "org": "ByteDance", "year": 2023, "description_en": "A vision-language manipulation policy that fine-tunes OpenFlamingo for robotic control on the CALVIN benchmark.", "description_ko": "CALVIN 벤치마크에서 로봇 제어를 위해 OpenFlamingo를 파인튜닝한 비전-언어 조작 정책.", "github_url": "https://github.com/RoboFlamingo/RoboFlamingo", "paper_url": "https://arxiv.org/abs/2311.01378", "hf_url": "", "project_url": "", "categories": ["manipulation"], "hardware": ["manipulator"], "learning": ["VLA"], "framework": ["pytorch"], "communication": [], "stats": {"github_stars": 429, "github_forks": 39, "hf_downloads": 0, "last_updated": "2026-04-19"}, "added_date": "2023-11-02", "tags": ["VLA", "CALVIN", "OpenFlamingo"]}, {"id": "gr1", "name": "GR-1", "org": "BAAI / Beijing Academy of AI", "year": 2024, "description_en": "Unleashing Large-Scale Video Generative Pre-Training for Visual Robot Manipulation. GPT-style pre-training on video then fine-tuned for robot control.", "description_ko": "시각 로봇 조작을 위한 대규모 비디오 생성 사전학습. 비디오에서 GPT 스타일 사전학습 후 로봇 제어로 파인튜닝.", "github_url": "https://github.com/bytedance/GR-1", "paper_url": "https://arxiv.org/abs/2312.13139", "hf_url": "", "project_url": "https://gr1-manipulation.github.io", "categories": ["manipulation"], "hardware": ["manipulator"], "learning": ["VLA", "IL"], "framework": ["pytorch"], "communication": [], "stats": {"github_stars": 309, "github_forks": 16, "hf_downloads": 0, "last_updated": "2026-04-19"}, "added_date": "2024-01-01", "tags": ["video-pretraining", "GPT", "manipulation"]}, {"id": "crossformer", "name": "CrossFormer", "org": "UC Berkeley / others", "year": 2024, "description_en": "A cross-embodiment transformer robot policy trained on 900k diverse robot trajectories. Flexible architecture for heterogeneous robots.", "description_ko": "90만 개의 다양한 로봇 궤적으로 학습된 교차 구현 트랜스포머 로봇 정책. 이기종 로봇을 위한 유연한 아키텍처.", "github_url": "https://github.com/rail-berkeley/crossformer", "paper_url": "https://arxiv.org/abs/2408.11812", "hf_url": "https://huggingface.co/rail-berkeley/crossformer", "project_url": "https://crossformer-model.github.io", "categories": ["manipulation"], "hardware": ["manipulator", "mobile"], "learning": ["IL", "VLA"], "framework": ["jax"], "communication": [], "stats": {"github_stars": 282, "github_forks": 35, "hf_downloads": 22, "last_updated": "2026-04-19"}, "added_date": "2024-08-21", "tags": ["cross-embodiment", "generalist", "transformer"]}], "datasets": [{"id": "open-x-embodiment", "name": "Open X-Embodiment (OXE)", "org": "Google DeepMind / RT-X Team", "year": 2023, "description_en": "A large-scale robot learning dataset aggregating 22 robot embodiments, 527 skills, and 1M+ robot trajectories from 21 institutions.", "description_ko": "21개 기관의 22개 로봇 구현체, 527개 기술, 100만 개 이상의 로봇 궤적을 통합한 대규모 로봇 학습 데이터셋.", "github_url": "https://github.com/google-deepmind/open_x_embodiment", "paper_url": "https://arxiv.org/abs/2310.08864", "hf_url": "https://huggingface.co/datasets/jxu124/OpenX-Embodiment", "project_url": "https://robotics-transformer-x.github.io", "categories": ["manipulation"], "hardware": ["manipulator", "mobile"], "source": ["real", "teleoperation"], "modality": ["rgb", "rgbd", "proprioception"], "scale": {"trajectories": 1000000, "hours": 3000, "environments": 527, "robots": 22}, "stats": {"github_stars": 1782, "hf_downloads": 13129, "last_updated": "2026-04-19", "github_forks": 112}, "added_date": "2023-10-13", "tags": ["large-scale", "multi-robot", "RT-X", "generalist"]}, {"id": "droid", "name": "DROID", "org": "UC Berkeley / Stanford / others", "year": 2024, "description_en": "A large-scale in-the-wild robot manipulation dataset with 350 hours of data collected across 564 scenes in 86 buildings using a Franka Panda arm.", "description_ko": "Franka Panda 팔로 86개 건물의 564개 장면에서 수집된 350시간 분량의 대규모 실제 환경 로봇 조작 데이터셋.", "github_url": "https://github.com/droid-dataset/droid", "paper_url": "https://arxiv.org/abs/2403.12945", "hf_url": "https://huggingface.co/datasets/droid-dataset/droid", "project_url": "https://droid-dataset.github.io", "categories": ["manipulation"], "hardware": ["manipulator"], "source": ["real", "teleoperation"], "modality": ["rgb", "rgbd", "proprioception"], "scale": {"trajectories": 76000, "hours": 350, "environments": 564, "robots": 1}, "stats": {"github_stars": 345, "hf_downloads": 0, "last_updated": "2026-04-19", "github_forks": 68}, "added_date": "2024-03-19", "tags": ["in-the-wild", "Franka", "large-scale", "diverse"]}, {"id": "bridgev2", "name": "BridgeData V2", "org": "UC Berkeley", "year": 2023, "description_en": "A large, diverse dataset of robotic manipulation behaviors collected in a tabletop kitchen environment. 60k+ demonstrations across 24 environments.", 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GPU-parallelized simulation.", "description_ko": "20개 태스크 패밀리와 2000개 이상의 다양한 객체를 포함하는 일반화 가능한 조작 기술을 위한 통합 벤치마크. 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GPU 병렬 시뮬레이션.", "github_url": "https://github.com/haosulab/ManiSkill", "paper_url": "https://arxiv.org/abs/2302.04659", "hf_url": "https://huggingface.co/datasets/haosulab/ManiSkill", "project_url": "https://maniskill.ai", "categories": ["manipulation", "dexterous"], "hardware": ["manipulator"], "source": ["simulation"], "modality": ["rgb", "rgbd", "proprioception"], "scale": {"trajectories": 36000, "hours": 0, "environments": 20, "robots": 8}, "stats": {"github_stars": 2797, "hf_downloads": 79, "last_updated": "2026-04-19", "github_forks": 466}, "added_date": "2023-02-09", "tags": ["benchmark", "GPU-parallel", "SAPIEN", "diverse-objects"]}, {"id": "language-table", "name": "Language-Table", "org": "Google", "year": 2023, "description_en": "A dataset and benchmark of 600k+ robot demonstrations with natural language instructions for pushing and rearranging tabletop blocks.", "description_ko": "테이블탑 블록 밀기와 재배치를 위한 자연어 지시와 함께 60만 개 이상의 로봇 시연 데이터셋 및 벤치마크.", "github_url": "https://github.com/google-research/language-table", "paper_url": "https://arxiv.org/abs/2210.01911", "hf_url": "https://huggingface.co/datasets/google/language_table", "project_url": "", "categories": ["manipulation"], "hardware": ["manipulator"], "source": ["real", "simulation", "teleoperation"], "modality": ["rgb", "proprioception"], "scale": {"trajectories": 600000, "hours": 0, "environments": 1, "robots": 1}, "stats": {"github_stars": 356, "hf_downloads": 0, "last_updated": "2026-04-19", "github_forks": 30}, "added_date": "2022-10-04", "tags": ["language-conditioned", "blocks", "tabletop", "large-scale"]}, {"id": "humanoidbench", "name": "HumanoidBench", "org": "CMU / UC San Diego / MIT", "year": 2024, "description_en": "A simulation benchmark for whole-body humanoid locomotion and manipulation with 27 tasks. 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Widely used for meta-learning and multi-task RL research.", "description_ko": "50개의 다양한 로봇 조작 태스크를 포함하는 멀티태스크 RL 벤치마크. 메타러닝 및 멀티태스크 RL 연구에 널리 사용됩니다.", "github_url": "https://github.com/Farama-Foundation/Metaworld", "paper_url": "https://arxiv.org/abs/1910.10897", "hf_url": "", "project_url": "https://meta-world.github.io", "categories": ["manipulation"], "hardware": ["manipulator"], "source": ["simulation"], "modality": ["proprioception"], "scale": {"trajectories": 0, "hours": 0, "environments": 50, "robots": 1}, "stats": {"github_stars": 1793, "hf_downloads": 0, "last_updated": "2026-04-19", "github_forks": 342}, "added_date": "2019-10-23", "tags": ["benchmark", "meta-learning", "multi-task", "Sawyer"]}, {"id": "roboagent", "name": "RoboAgent Dataset", "org": "CMU", "year": 2023, "description_en": "A dataset for scalable data-efficient learning of robotic manipulation strategies. 7,500 demonstrations covering 38 tasks with semantic augmentation.", "description_ko": "로봇 조작 전략의 확장 가능하고 데이터 효율적인 학습을 위한 데이터셋. 의미론적 증강으로 38개 태스크에 걸쳐 7,500개의 시연.", "github_url": "https://github.com/rail-berkeley/roboagent", "paper_url": "https://arxiv.org/abs/2309.01918", "hf_url": "", "project_url": "https://robopen.github.io", "categories": ["manipulation"], "hardware": ["manipulator"], "source": ["real", "teleoperation"], "modality": ["rgb", "proprioception"], "scale": {"trajectories": 7500, "hours": 0, "environments": 38, "robots": 1}, "stats": {"github_stars": 390, "hf_downloads": 0, "last_updated": "2026-04-19"}, "added_date": "2023-09-05", "tags": ["semantic-augmentation", "data-efficient", "Franka"]}], "tools": [{"id": "mujoco", "name": "MuJoCo", "org": "DeepMind / Google", "year": 2022, "description_en": "Open-source physics engine for contact-rich robot simulation. Industry standard for locomotion and manipulation research, with high-fidelity rigid body dynamics. Open-sourced by DeepMind in 2022.", "description_ko": "접촉이 많은 로봇 시뮬레이션을 위한 오픈소스 물리 엔진. 고충실도 강체 역학으로 보행 및 조작 연구의 업계 표준. 2022년 DeepMind가 오픈소스화.", "github_url": "https://github.com/google-deepmind/mujoco", "paper_url": "https://arxiv.org/abs/2103.09615", "project_url": "https://mujoco.org", "type": "physics_engine", "gpu_accelerated": false, "ros_support": false, "language": ["c", "python", "c++"], "stats": {"github_stars": 8700, "last_updated": "2026-04-12"}, "added_date": "2022-10-18", "tags": ["physics-engine", "contact-rich", "locomotion", "manipulation", "standard"]}, {"id": "isaac-lab", "name": "Isaac Lab", "org": "NVIDIA", "year": 2023, "description_en": "GPU-accelerated robot learning framework built on NVIDIA Isaac Sim. Supports RL, imitation learning, and sim-to-real transfer across manipulators, humanoids, and quadrupeds. 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RTX 4090 단일 GPU에서 4300만 FPS 달성. 강체/연체/유체 시뮬레이션 및 자연어 기반 씬 생성 지원.", "github_url": "https://github.com/Genesis-Embodied-AI/Genesis", "paper_url": "https://arxiv.org/abs/2501.00599", "project_url": "https://genesis-world.readthedocs.io", "type": "physics_engine", "gpu_accelerated": true, "ros_support": false, "language": ["python"], "stats": {"github_stars": 28523, "last_updated": "2026-04-12"}, "added_date": "2024-12-01", "tags": ["simulation", "physics-engine", "generative", "ultra-fast"]}, {"id": "simplerenv", "name": "SimplerEnv", "org": "UC Berkeley / Google", "year": 2024, "description_en": "Simulation benchmark for evaluating real-robot policies in controlled environments. Built on MuJoCo/SAPIEN to reproduce real-world robot setups and provide reproducible evaluation of VLA/manipulation policies.", "description_ko": "제어된 환경에서 실제 로봇 정책을 평가하기 위한 시뮬레이션 벤치마크. 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Robosuite를 확장하여 다양한 주방/가정 씬, 100개 이상 객체 카테고리, 언어 조건부 태스크 생성 제공.", "github_url": "https://github.com/robocasa/robocasa", "paper_url": "https://arxiv.org/abs/2406.02523", "project_url": "https://robocasa.ai", "type": "full_stack", "gpu_accelerated": false, "ros_support": false, "language": ["python"], "stats": {"github_stars": 1400, "last_updated": "2026-04-12"}, "added_date": "2024-06-04", "tags": ["household", "generalist", "language-conditioned", "manipulation"]}, {"id": "brax", "name": "Brax", "org": "Google", "year": 2021, "description_en": "JAX-based GPU/TPU-accelerated differentiable physics engine for locomotion and continuous control. 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