Teaching robots to manipulate from fake data that actually works

Robots trained entirely in simulation usually fail in the real world because pixels and physics differ. HyperSim bridges this gap through three mechanisms: ultra-realistic simulated environments, adversarial trajectory generation to stress-test policies, and joint training on both simulated and real data. Tested on 400 real-world manipulation tasks across two policy architectures, the system reached 80–95% success rates and proved significantly more robust to unexpected pushes and disturbances.