Source: Upstartsmedia
A startup is using game engines to train robotic locomotion, but the gap between simulation and physical space remains stubbornly real—the robot still can't reliably navigate a glass wall it should theoretically understand. This exposes a core problem in embodied AI: synthetic training data doesn't capture the friction, reflectivity, and spatial ambiguity of actual environments, forcing teams into expensive real-world iteration cycles that undercut the efficiency gains of simulation-based approaches. Until sim-to-real transfer solves edge cases like transparent obstacles, robots trained primarily in games will remain limited to controlled settings rather than general deployment.