NVIDIA tech helps humanoid robot beat human operators at opening doors

NVIDIA researchers have developed “DoorMan,” a robotic learning system enabling a humanoid robot—the $16,000 Unitree G1—to open doors more efficiently than human operators. Utilizing only built-in RGB cameras and trained entirely through simulation-based reinforcement learning in NVIDIA’s Isaac Lab, the system allows the robot to open various real-world doors faster and with higher success rates than humans remotely controlling it. In tests, DoorMan completed door-opening tasks up to 31% faster than expert teleoperators and achieved an 83% success rate, outperforming both expert (80%) and non-expert (60%) human operators. This advancement represents significant progress in “loco-manipulation,” where robots must simultaneously walk, perceive, coordinate limbs, and manipulate objects. The DoorMan system employs a novel pixel-to-action training approach, relying solely on raw RGB input without specialized sensors like depth cameras or motion-capture markers. To overcome common reinforcement learning challenges, the researchers introduced a “staged-reset” mechanism