Magiclab Embodied AI Robots for Research & Education

Support humanoid robot research in locomotion control, perception, interaction, embodied AI algorithms, and real-world task execution.

Enable students to learn robotics, AI perception, motion planning, and human-robot interaction through hands-on robot experiments.

Build practical training environments for robot assembly, debugging, operation, maintenance, and application deployment.

Use MagicHand-based platforms for grasping, picking, placing, tool handling, and fine manipulation skill training.

Provide open APIs and development tools for secondary development, task programming, simulation, and scenario-based robot applications.

Support multimodal data collection, annotation, teleoperation, model training, and embodied intelligence dataset construction.

Help schools build competition-oriented training projects covering robot control, perception, navigation, and collaborative tasks.

Connect education, research, and industry projects to accelerate technology validation, talent training, and application transformation.