The Industrial Application Layer of the Physical AI Era.
大模型时代,Cursor、Harvey 把 token 变成生产力。物理 AI 时代,我们做同样的事——把基模的 Action,变成真实场景里稳定运行的生产力。整机 + 智能,把具身智能真正用进制造、物流、能源等真实场景。In the LLM era, Cursor and Harvey turned tokens into productivity. In the Physical AI era we do the same — we turn a foundation model's Action into stable, real-world productivity. Embodiment + intelligence, bringing embodied AI into real manufacturing, logistics and energy scenarios.
大量作业仍停在"人 + 半自动化设备"协作:多品种混线、频繁换型、依赖判断和手感。固定专机做不了,通用机器人也通用不了。A huge amount of work is still stuck at "humans + semi-automation": mixed models, frequent changeovers, reliance on judgment and feel. Fixed machines can't handle it; general robots can't generalize to it.
大批量标准件交给专机;我们专注传统自动化做不进、必须靠人柔性的那类工序。High-volume standard parts go to dedicated machines; we focus on the processes that need human flexibility, where traditional automation can't reach.
而且不只工业产线。凡是这类"靠人柔性"的场景,都是我们的战场——And not only production lines. Any scenario that still relies on human flexibility is our field —
打磨、检测、装配、上下料Grinding, inspection, assembly, loading
分拣、装卸、多机调度Sorting, handling, multi-robot dispatch
能源装备、矿洞、数据中心Energy equipment, mining, data centers
基模越强,应用层越值钱 —— 那层"胶水",把分散的能力,变成能干活的部署。The stronger the base models, the more valuable the application layer — the glue that turns scattered capability into working deployments.
围绕真实场景做采集、仿真、标注,沉淀"面向具身 AI 训练的真实世界操作数据"——视觉、力反馈、动作轨迹,仿真造不出、别人买不到,可开源共享。We capture, simulate and label around real scenarios to build real-world operation data for embodied-AI training — vision, force feedback, motion trajectories that simulation can't create and others can't buy. Partly open-sourced.
不训基模。用 RL + 人在环把通用基模调成场景专用能力,把成功率从"能跑"训到"能交付"——这是落地成败真正发生的地方。We don't train foundation models. With RL + human-in-the-loop we tune a general base model into scenario-specific capability, lifting success rates from "it runs" to "it delivers" — where deployment is truly won or lost.
工作站本体、柔性工装、末端执行器,加上多机 · 多设备的实时协同控制(节拍、防干涉、坐标系),组合成一台可交付的整机。Workstation bodies, flexible fixtures and end-effectors, plus real-time multi-robot / multi-device coordination (cycle time, anti-collision, coordinate frames) — assembled into one deliverable machine.
从场景分析、能力拆解到落地交付的完整方案——把具身智能真正用进你的生产,不是给你一个 demo。A complete solution from scenario analysis and capability breakdown to delivery — bringing embodied AI into your production, not just a demo.
联合真实场景,采集视觉、力反馈、动作轨迹等一手操作数据。First-hand operation data — vision, force, trajectories — from real scenarios.
模块化仿真,低成本扩展长尾工艺与节拍,用于预训练与迁移。Modular sim to cheaply expand long-tail processes, for pretraining and transfer.
质量闭环,把原始数据清洗成可直接训练的高质量数据。A quality loop that turns raw data into training-ready data.
沉淀垂类场景数据集,一部分开源共享,连接生态。Vertical datasets, partly open-sourced to connect the ecosystem.
场景和数据两个轮子互相带动:越用越懂你的产线 —— 这是买一台设备得不到的东西。Scenarios and data drive each other: the longer it runs, the better it fits your line — something you never get from buying a machine.
把具身智能落到真实场景,不是训好一个模型就行 —— 它是一整套系统工程,要同时具备四种能力。纯基模厂只有 AI,纯集成商只有产线;四样同时具备,才做得成。Bringing embodied AI into the real world isn't just training a model — it's a full systems-engineering effort that needs four capabilities at once. A pure model shop has only AI; a pure integrator has only the line. You need all four to pull it off.
把每个场景拆到底层能力需求,配出对的方案。Break each scenario down to its capability needs, then design the right solution.
RL + 人在环,把通用基模训成场景可交付。RL + human-in-loop to train a base model into deliverable capability.
具身机器人 + 工业设备 + PLC 协同成一条线。Embodied robots + industrial equipment + PLC working as one line.
现场部署、调试、稳定运行,真正交付到生产。On-site deploy, tune and run stably — delivered into production.
合心集团既是我们的天使投资方,也是我们的产线客户 —— 一边投,一边下场用。这是工业 Physical AI 公司最理想的早期验证。HEXIN Group is both our angel investor and a production-line customer — investing and using at the same time. The ideal early validation for an industrial Physical AI company.
把具身智能真正落进一个场景,从来不是单点技术能解决的 —— 它是一整套系统工程:从场景数据采集、真机后训练与技能沉淀,到多机多设备协同控制、模块化整机,再到现场集成落地,每一环都不能缺。Bringing embodied AI into a real scenario is never solved by a single technology — it is a full systems-engineering effort: from scenario data capture, real-machine post-training and skill libraries, to multi-robot / multi-device coordination, modular hardware, and on-site deployment. Every link has to be there.
星炬是一支清华系团队,同时具备顶尖 AI 与强化学习算法能力、具身智能研究能力,以及多年产业工程经验。在真实场景的 AI 方案落地上,我们积累了超过十年的经验,也最擅长这件事 —— 一整套系统工程里要补的每一环,都在我们的能力范围之内。Xingju is a Tsinghua-rooted team combining top-tier AI and reinforcement-learning talent, embodied-AI research, and years of industrial engineering. In deploying AI solutions into real-world scenarios we carry over a decade of experience — and it is what we do best. Every link a full systems-engineering effort demands sits within our reach.
凡是"必须靠人的柔性、传统自动化做不进"的场景,把它发给我们,一起看能不能做。Any scenario that still needs human flexibility and can't be automated the old way — send it over and let's see if we can do it.