Physen AI

Summary

About

About Physen AI

Physen combines wearable capture hardware with a software pipeline for turning human physical interaction into structured robot-learning data. EgoScale and EgoForce record egocentric vision, motion and force signals; ForceAlign maps those observations across embodiments. Physen Studio supports collection and dataset operations, while TaF-VLA and ForceBench provide model and evaluation layers around the same multimodal data workflow.

The Shenzhen company is led by researchers and engineers with backgrounds in multimodal motion perception, optics, robotics and vision-language models. Its Three-Ring collection approach uses a head-worn visual-positioning device and two arm-worn sensing units to capture synchronized demonstrations without instrumenting a target robot. In August 2026, Physen disclosed an angel financing to move its acquisition equipment and data platform into mass production and delivery. The approach is intended for robotics OEMs, model labs, industrial-automation teams and research groups that need scalable real-world interaction data.

PRICE
Range 1Y
Return
High
Low
Updated
DATA: KORTHOS

Articles

09 AUG 2026

Robotics capital moves — 3–9 August 2026

Funding

Investors

  • Leading Optoelectronic - Participant - Angel / Individual - 1 funding event - first seen 2026-08-06
  • Mingyao Huichuang - Participant - Angel / Individual - 1 funding event - first seen 2026-08-06
  • Shanjing No. 1 Fund - Participant - VC - 1 funding event - first seen 2026-08-06
  • SwitchBot - Participant - Unknown - 1 funding event - first seen 2026-08-06
  • Worth Star Torch Fund - Lead - VC - 1 funding event - first seen 2026-08-06

Team

  • Huang Zhishen - Founder - Sets Physen's product and research direction across interaction-data capture, alignment and commercialization.
  • Liang Yu - Hardware Product Lead - Leads the wearable sensing hardware used to capture synchronized human interaction data.
  • Wang Chen - Optics and Robotics Lead - Directs the visual and sensing architecture behind Physen's capture equipment.
  • Zhao Wentao - Robot Engineering Lead - Connects Physen's capture hardware and data-alignment stack to target robot embodiments.
  • Chen Lei - Vision-Language Model and Semantic Understanding Lead - Leads model research that connects multimodal interaction data with robot-learning objectives.

Timeline

Events

  • 2026-08-06 - Funding - Physen AI raises multi-million-yuan angel round

    Shanjing No. 1 Fund and Mingyao Huichuang followed on, while Leading Optoelectronic and SwitchBot invested directly. Physen will use the capital for mass production and delivery of its Three-Ring data-acquisition equipment and development of its data-algorithm platform. The company captures synchronized vision, force and motion data from human demonstrations and aligns it for use across robot embodiments.

    Source

Context

Comparable profiles

  • Vision Lab

    stack adjacency - Embodied Data Capture · Physical AI Data Infrastructure

  • MANUS

    stack adjacency - Embodied Data Capture · Physical AI Data Infrastructure

  • XDOF

    stack adjacency - Embodied Data Capture · Physical AI Data Infrastructure

  • Lightwheel

    stack adjacency - Physical AI Data Infrastructure

  • LabyrinthAI

    stack adjacency - Physical AI Data Infrastructure

  • Rokoko

    stack adjacency - Embodied Data Capture

  • StretchSense

    stack adjacency - Embodied Data Capture

  • Maniformer

    stack adjacency - Embodied Data Capture

  • Jiiov / 极豪科技

    stack adjacency - Embodied Data Capture

  • Wuwen Zhike

    stack adjacency - Physical AI Data Infrastructure

  • Realset AI

    stack adjacency - Embodied Data Capture

  • Noitom

    stack adjacency - Embodied Data Capture

Shared workflows

Context tags

  • Physical AI data infrastructure - Stack Layer - Physen supplies a hardware and software data-infrastructure layer for physical-AI training workflows.
  • Embodied data capture - Stack Layer - Physen captures synchronized human vision, force and motion demonstrations for embodied AI.
  • Robot learning data collection - Workflow - The company's systems collect interaction data for robot-policy and model training.
  • Force and contact interaction - Capability - EgoForce and the Three-Ring system capture force and contact information alongside vision and movement.