[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-81013":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":8,"language":10,"languages":8,"totalLinesOfCode":8,"stars":11,"forks":12,"watchers":13,"openIssues":14,"contributorsCount":15,"subscribersCount":15,"size":15,"stars1d":16,"stars7d":16,"stars30d":12,"stars90d":15,"forks30d":15,"starsTrendScore":17,"compositeScore":18,"rankGlobal":8,"rankLanguage":8,"license":19,"archived":20,"fork":20,"defaultBranch":21,"hasWiki":20,"hasPages":20,"topics":22,"createdAt":8,"pushedAt":8,"updatedAt":23,"readmeContent":24,"aiSummary":25,"trendingCount":15,"starSnapshotCount":15,"syncStatus":16,"lastSyncTime":26,"discoverSource":27},81013,"rio","robot-i-o\u002Frio","robot-i-o",null,"https:\u002F\u002Frobot-i-o.github.io\u002F","Python",32,3,29,1,0,2,6,44.11,"Apache License 2.0",false,"main",[],"2026-06-12 04:01:31","\u003Cp align=\"center\">\n  \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fastral-sh\u002Fuv\">\n  \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fendpoint?url=https:\u002F\u002Fraw.githubusercontent.com\u002Fastral-sh\u002Fuv\u002Fmain\u002Fassets\u002Fbadge\u002Fv0.json\" \u002F>\u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fastral-sh\u002Fruff\">\n  \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fendpoint?url=https:\u002F\u002Fraw.githubusercontent.com\u002Fastral-sh\u002Fruff\u002Fmain\u002Fassets\u002Fbadge\u002Fv2.json\" \u002F>\u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fastral-sh\u002Fty\">\n  \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fendpoint?url=https:\u002F\u002Fraw.githubusercontent.com\u002Fastral-sh\u002Fty\u002Fmain\u002Fassets\u002Fbadge\u002Fv0.json\" \u002F>\u003C\u002Fa>\n\u003C\u002Fp>\n\n# RIO\n\nRIO: flexible real-time Robot I\u002FO for cross-embodiment robot learning.\n\nThis project provides a Python-based interface to use different robot arms (Franka, Kinova, Universal Robots, UFACTORY, SO100, ...), grippers, cameras, and teleop interfaces, with built-in support for data collection, teleoperation, and Vision-Language-Action (VLA) policy deployment.\n\n## Setup\n\nTested on Ubuntu 22.04 LTS with an optional real-time kernel patch. See [`docs\u002Fubuntu.md`](docs\u002Fubuntu.md) for setup instructions.\n\n```bash\ngit clone git@github.com:cmubig\u002Frio.git\n\n# install open-pi\ncd rio\nmkdir third_party && cd third_party\ngit clone git@github.com:Physical-Intelligence\u002Fopenpi.git\n\n# install uv\ncurl -LsSf https:\u002F\u002Fastral.sh\u002Fuv\u002Finstall.sh | sh\n\n# create venv and install dependencies\nuv venv --python 3.10\nsource .venv\u002Fbin\u002Factivate\nuv sync --all-extras\n```\n\n## Documentation\n\nBuild and browse the docs locally at http:\u002F\u002Flocalhost:8000:\n\n```bash\nuv run mkdocs serve\n```\n","RIO 是一个为跨实体机器人学习设计的灵活实时机器人输入输出接口。该项目基于 Python，支持多种机械臂（如 Franka、Kinova、Universal Robots 等）、夹爪、摄像头以及远程操作界面，并内置了数据收集、远程操控和视觉-语言-动作策略部署功能。RIO 适用于需要高效整合不同硬件进行实验或开发的研究人员及工程师，尤其是在涉及复杂多样的机器人系统集成场景中。通过提供统一且易于扩展的接口，RIO 大大简化了跨平台机器人的软件开发流程。","2026-06-11 04:03:10","CREATED_QUERY"]