Robots For Real-World Work: Training Challenges And How To Solve Them

Walmart's robots are trained with diverse real-world data to handle inconsistent lighting, surfaces, and object placement. Sensory variability causes performance drops when real-world conditions differ from simulation. Robots use sim-to-real transfer and adaptive learning to update continuously from live feedback. Walmart underinvests in simulation and digital twin infrastructure. Low-fidelity clean simulations fail to capture real-world variability and do not prepare robots for unpredictable environments. This approach improves robot resilience in warehouse and factory operations.