[{"data":1,"prerenderedAt":127},["ShallowReactive",2],{"output-content-zh-mimiclite":3},{"id":4,"title":5,"body":6,"description":114,"extension":115,"meta":116,"navigation":122,"path":123,"seo":124,"stem":125,"__hash__":126},"outputs\u002Foutputs\u002Fzh\u002Fmimiclite.md","MimicLite",{"type":7,"value":8,"toc":96},"minimark",[9,13,17,23,27,30,33,36,39,42,46,49,52,55,58,61,65,68,71,74,77,80,83,86,89,92],[10,11,12],"h2",{"id":12},"核心亮点",[14,15,16],"p",{},"MimicLite 是 RoboParty Lab 首批公开的开源 codebase：用 8 GPU 和约 1\u002F500 的 SONIC 算力训练出接近 SONIC 级别的人形机器人通用运动跟踪策略，并打通数据、资产、训练、遥操作与 Sim2Real 部署全链路。",[18,19,20],"blockquote",{},[14,21,22],{},"首个开源 codebase MimicLite 上线：8 GPU 训练 SONIC 级人形机器人通用运动跟踪策略，打通数据、资产、训练、遥操作与 Sim2Real 部署全链路。",[24,25],"metric-strip",{":items":26},"[{\"label\":\"训练资源\",\"value\":\"8 GPU\",\"description\":\"用于核心训练的资源规模。\",\"icon\":\"i-lucide-cpu\"},{\"label\":\"训练时间\",\"value\":\"2 小时\",\"description\":\"用于验证轻量训练路径。\",\"icon\":\"i-lucide-clock\"},{\"label\":\"算力规模\",\"value\":\"1\u002F500\",\"description\":\"约为 SONIC 训练算力的 1\u002F500。\",\"icon\":\"i-lucide-gauge\"},{\"label\":\"遥操作延迟\",\"value\":\"0.1 秒\",\"description\":\"Pico 低延迟遥操作目标。\",\"icon\":\"i-lucide-radio-tower\"}]",[10,28,29],{"id":29},"项目简介",[14,31,32],{},"MimicLite 把 motion data、robot assets、reinforcement learning、teleoperation 和 Sim2Real deployment 组织成一套可复现、可复用的开源 codebase。它不只发布训练脚本，也公开围绕人形机器人运动跟踪所需的基础工程链路。",[10,34,35],{"id":35},"相关工作",[14,37,38],{},"MimicLite 与 SONIC、mjlab \u002F IsaacLab、PPO \u002F SAC、humanoid-gpt、twist2、teleopit 等训练与部署路线相关。它的价值在于把这些路线中分散的训练、资产、数据格式和部署适配成本，收敛到更统一的 codebase 中。",[10,40,41],{"id":41},"技术方法",[43,44,45],"h3",{"id":45},"快速可扩展训练",[14,47,48],{},"MimicLite 仅使用 SONIC 约 1\u002F500 级别的训练算力，用 8 卡 H200 训练约 2 个小时，即可在根部追踪等关键指标上达到接近 SONIC 的效果。系统支持 mjlab 与 IsaacLab 仿真后端，并兼容 PPO 和 SAC 强化学习算法。",[43,50,51],{"id":51},"运动跟踪基础设施",[14,53,54],{},"mjhub 用于统一管理机器人 asset，降低机器人模型资产维护成本；any4hdmi 用于 motion dataset 的 convert、process、visualize 和 load，将 LAFAN、100STYLE、SONIC、Real 等动作数据统一成存储高效的 HDMI 格式，并支持自动缓存前向运动学结果。",[43,56,57],{"id":57},"低延迟遥操作",[14,59,60],{},"MimicLite 支持约 0.1 秒端到端延迟的 Pico 低延迟遥操作。Pico \u002F XR 输入被实时转换为参考动作，策略根据机器人状态和参考运动输出低层控制目标，并可以在 MuJoCo 和真实机器人之间复用同一套接口。",[43,62,64],{"id":63},"模块化-sim2real","模块化 Sim2Real",[14,66,67],{},"Sim2Real 部署侧支持自定义 observation function 作为 policy 输入。Agent 可以追踪外部训练代码，实现对应 obs function，并生成 deploy YAML，从而在 10 分钟级别把外部 codebase 训练出的 policy 接入 MimicLite 的部署链路。",[10,69,70],{"id":70},"评估结果",[14,72,73],{},"公开文案中，MimicLite 已在根部追踪等关键指标上达到接近 SONIC 的效果，并在真机上支持转身、侧步、折返跑、低姿态下蹲、跪地起身等连续动作切换。后续会继续补充训练曲线、动作完成度、资源消耗和不同机器人本体上的表现。",[10,75,76],{"id":76},"讨论",[14,78,79],{},"MimicLite 的价值不只在训练结果，也在于统一数据、资产、训练后端、policy artifact 和部署链路。它可以作为跨 codebase policy 统一评测和真机部署的适配层，减少研发过程中重复编写 glue code 的成本。",[10,81,82],{"id":82},"结论",[14,84,85],{},"MimicLite 将高成本的人形机器人运动跟踪训练，整理成更轻量、更模块化、更适合开源协作的 codebase 和 infra，是 RoboParty Lab 首批公开开放样本之一。",[10,87,88],{"id":88},"资源链接",[14,90,91],{},"GitHub、训练配置、示例数据、模型权重、技术报告和引用信息将在正式公开后持续补充。",[93,94],"resource-links",{":links":95},"[{\"label\":\"GitHub\",\"href\":\"https:\u002F\u002Fgithub.com\u002FjaggerShen\",\"icon\":\"i-simple-icons-github\",\"external\":true},{\"label\":\"项目主页\",\"href\":\"\u002Fprojects\u002Fmimiclite\",\"icon\":\"i-lucide-globe\"}]",{"title":97,"searchDepth":98,"depth":98,"links":99},"",2,[100,101,102,103,110,111,112,113],{"id":12,"depth":98,"text":12},{"id":29,"depth":98,"text":29},{"id":35,"depth":98,"text":35},{"id":41,"depth":98,"text":41,"children":104},[105,107,108,109],{"id":45,"depth":106,"text":45},3,{"id":51,"depth":106,"text":51},{"id":57,"depth":106,"text":57},{"id":63,"depth":106,"text":64},{"id":70,"depth":98,"text":70},{"id":76,"depth":98,"text":76},{"id":82,"depth":98,"text":82},{"id":88,"depth":98,"text":88},"8 GPU 训练 SONIC 级人形机器人通用运动跟踪策略，打通数据、资产、训练、遥操作与 Sim2Real 部署全链路。","md",{"slug":117,"type":118,"status":119,"coverImage":120,"linkUrl":121,"published":122},"mimiclite","Codebase","preview","\u002Fimages\u002Fjoin-us\u002Fjoin-us-hero-poster.jpg","\u002Foutputs#mimiclite",true,"\u002Foutputs\u002Fzh\u002Fmimiclite",{"title":5,"description":114},"outputs\u002Fzh\u002Fmimiclite","j7W88vzFvgqZHCuEKGywKXpHdac9tAChAmgeqLAjr5U",1782988731098]