Embodied-AI simulation training

为什么ROI极高

High-fidelity physics simulation and scenario authoring accelerate policy training and sim-to-real transfer while cutting hardware wear.

Loss = real-robot trial hours × wear cost + training delay × delivery opportunity cost

行业痛点

• Expensive real trials — Humanoid / mobile robot on-hardware training is wear-heavy and risky.
• Hard scenario coverage — Long-tail conditions are difficult to reproduce at scale in the real world.
• Slow transfer — Sim-to-real gaps force repeated tuning.

闭环方案

Start with high-fidelity scenario authoring and skill training, then close sim → evaluate → transfer loops.

可量化收益

Figures reflect public industry / landmark magnitudes for value density, not GINHE project commitments.

切入策略

Start with one skill or one work cell in simulation to prove training throughput and transfer quality.

Typical POC: 4–8 weeks.