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The archive · AI & Models · Strategic decision · 2024–2026

PsiBot raised 2 billion yuan in 1.5 years, betting on data gloves.

PsiBot, founded in late 2024, disclosed 2 billion yuan financing, betting on data gloves for embodied intelligence.

PsiBot (灵初智能)

The betBetting data, not robot body, is key: data gloves collect data at 1/10 cost, feeding VLA for logistics.Building

What the business is

Providing an embodied intelligence 'brain' for logistics/retail scenarios: end-to-end VLA models plus self-developed data collection gloves and dexterous hands; after customer deployment, on-site data flows back to feed the models.

Starting capitalAngel round + Pre-A round cumulative about 2 billion yuan RMB (20亿元人民币) (first disclosed in March 2026); seed round invested by Hillhouse Venture Capital (高瓴创投), BlueRun Ventures (蓝驰创投), and AgiBot (智元机器人).

How it started

At the end of 2024, Wang Qibin (王启斌) (who had been responsible for Yunji Technology (云迹科技) hotel delivery robots and JD Logistics (京东物流) robot R&D) and post-2000s Chen Yuanpei (陈源培) (Peking University (北大) Yang Yaodong (杨耀东) team, Stanford (斯坦福) Fei-Fei Li (李飞飞) lab) and others founded PsiBot (灵初智能). The seed round received investment from Hillhouse Venture Capital (高瓴创投), BlueRun Ventures (蓝驰创投), and AgiBot (智元机器人), and the company co-established an embodied dexterous manipulation joint laboratory with Peking University (北大). The team judged: simulation data has a sim-to-real gap, real-robot teleoperation is too expensive, and what embodied intelligence truly lacks is low-cost, large-scale real interaction data.

What happened

In the second half of 2025, the company pivoted internally: it stopped pure demonstration-type demos and fully shifted to real data collection and segmented scenario delivery; it successively completed the angel round (China Development Fund (国开金融), Guozhong Capital (国中资本), CCTV Converged Media (央视融媒体) and other 'national team' (国家队) and industrial capital) and the Pre-A round (led by Xuhui Capital (徐汇资本)), cumulatively about 2 billion yuan (20亿), with valuation rising about 6-7 times in one year. It self-developed the Psi-SynEngine multimodal data glove; collection cost is about 1/10 of real-robot teleoperation; in customer warehouses' clothing package supply scenario it achieved generalization on more than 1,000 items of clothing and a maximum of 800 UPH, entering the 'production accompaniment' stage.

How it ended up

Still expanding: in 2026, it plans to scale shipment/delivery for three logistics scenarios—clothing package supply, box-in inspection, and sorting wall—build the largest dexterous hand dataset in China, and then extrapolate to more scenarios.

Background

PsiBot, founded in late 2024, team includes Wang Qibin (20 years robot ops) and Chen Yuanpei (PKU, Stanford). Seed from Hillhouse, BlueRun, AgiBot. Positioned as embodied brain: VLA model, gloves, dexterous hands; lands in logistics, no complete machines.

Bet: data is bottleneck. Simulation has gap, teleoperation costly. PsiBot uses human-native data via gloves at 1/10 cost, training transferable models, forming flywheel.

In H2 2025, shifted to real data and segmented delivery; disclosed 2 billion yuan financing, valuation up 6-7x. Clothing supply achieved 1000+ items, 800 UPH, entering production accompaniment; plans scaled delivery and largest dexterous hand dataset.

What has to be true

  • Bet clearly on data, not robot body, addressing key bottleneck.
  • Data gloves cut cost to 1/10, making large-scale data feasible.
  • Restrained scenario choice: logistics segments first, then extrapolate.
  • Rare capital: national and industrial investors, valuation up 6-7x in a year.

What can be applied

When tech routes are unsettled, bet on cost and data feedback: PsiBot cut data cost to 1/10, making iteration compound.

Aftermath

As of March 10, 2026, PsiBot completed about 2 billion yuan financing, moving to unicorn; validated clothing supply and box inspection at real sites, max 800 UPH, entering production accompaniment. Plans scaled delivery for three scenarios in 2026, release larger dataset by end of March, build largest dexterous hand dataset; adheres to small full stack, no wheeled chassis.

Sources

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