The Beijing Humanoid Robot Innovation Center needed more scalable compute, better embodied-intelligence data production, and stronger model reasoning support to build a general-purpose platform that could work across robot bodies, tasks, and scenarios.
Reported impact
The center improved embodied-model development efficiency, strengthened robot decision-making, and accelerated creation of a large national embodied-data asset for future robotics applications.
The Beijing Humanoid Robot Innovation Center used Baidu AI Cloud's BaiGe GPU compute platform, Qianfan large-model capabilities, and data-collection and labeling services to accelerate embodied-intelligence development. BaiGe provides the AI infrastructure layer that schedules compute efficiently and improves model training and inference performance for the center's daily work on the 'Hui Si Kai Wu' general embodied-intelligence platform. Qianfan and ERNIE-class model capabilities add higher-level reasoning by parsing natural-language instructions, fusing multimodal context, and decomposing complex tasks into executable subtask sequences. At the same time, Baidu's data-services team contributes to the center's national-scale embodied-data program by helping produce and label high-density, high-quality multimodal datasets.
Embodied data from multiple scenes and robot forms is collected and labeled, then fed into the center's training pipelines on BaiGe. Qianfan-backed models interpret instructions and support decision making, and the resulting platform improvements are fed back into new data collection and training cycles.
BaiGe provided high-performance, stable AI infrastructure that improved training and inference performance for the Hui Si Kai Wu platform (original: 百度智能云为北京人形机器人创新中心提供了高性能、稳定可靠的AI基础设施，实现算力高效调度并显著提升大模型的训推性能).
Baidu's data-service team is helping the center accelerate construction of a million-scale, high-quality embodied dataset (original: 百度智能云正全力助力北京人形机器人创新中心建设百万量级的高密度、高质量、高通用性数据集).
Qianfan and ERNIE-class models can parse natural-language instructions, fuse multimodal context, and break complex tasks into executable subtasks (original: 可实现自然语言指令的精准解析，并通过多模态信息融合与上下文理解，将复杂任务分解为可执行的子任务序列).
The center used Baidu AI Cloud's BaiGe GPU platform, Qianfan large-model platform, and embodied data-labeling services to improve model development efficiency, decision capability, and shared dataset creation (original: 依托百度智能云“百度百舸GPU算力平台”“千帆大模型平台”及“具身数据采标服务”，实现具身模型开发效率提升、智能决策能力强化及高质量数据集共建).
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