Research

Core Research Directions

Three interconnected research themes and four dedicated programs that translate industrial embodied intelligence into deployable systems—from dexterous manipulation and physics-informed manufacturing to human–machine collaboration.

Research Themes

RAIDS builds on three interconnected pillars in AI for industrial digital servitization.

  • Human-Robot Collaborative Manufacturing

    Mutual-cognitive collaboration, vision-language action models, mixed reality, and safe motion generation for human-centric smart manufacturing.

  • Smart Product-Service Systems

    Digital twins, intelligent design, and service-dominant logic toward adaptive, data-driven industrial product-service solutions.

  • Industrial Embodied Intelligence

    Embodied AI, contact-rich robot learning, and physics-informed methods that bridge perception, cognition, and execution on the factory floor.

Research Programs

Dedicated tracks translating our themes into deployable systems, platforms, and field demonstrations.

01

AeroDexter

  • Aerial dexterous manipulation
  • UAV–arm co-design
  • Contact-rich flight control
  • Embodied aerial intelligence

AeroDexter co-designs micro-UAVs with lightweight manipulators for contact-rich dexterous tasks in hard-to-reach spaces—unifying flight dynamics, force control, and embodied learning toward terrestrial-grade manipulation in free three-dimensional environments.

02

Cooland

  • Physics-Informed AI
  • Aerospace manufacturing
  • Human-in-the-Loop
  • Flexible production

COOLAND advances intelligent manufacturing for aerospace and high-end equipment through Physics-Informed AI and real-time physical field modeling. An operator-centric Human-in-the-Loop framework, paired with Intelligent Flexible Manufacturing, enables agile reconfiguration of assembly and machining lines.

03

Glovity

  • Dexterous manipulation
  • TeleX platform
  • Egocentric sensing
  • Skill transfer

Glovity develops precise, dexterous manipulation policies for contact-rich industrial tasks, and translates them through TeleX—proprietary egocentric sensing, multimodal capture, and learning pipelines that turn expert skill into deployable autonomy.

04

HumVe

  • Human-vehicle interaction
  • Advanced driver assistance
  • Foundation models
  • Multimodal perception

HumVe advances human–vehicle interaction for advanced driver assistance systems through large foundation models and cognitive reasoning. A human-centric AI framework, paired with real-time multimodal perception, enables vehicles to understand driver intent and environmental context, ensuring seamless and safe collaboration on the road.