The Stanford researchers took a different approach

The Stanford researchers took a different approach. They first used a reinforcement AI model to train a custom designed robot, called “HumanPlus” on 40 hours of various human motion data. They could then take the base lessons learned by training the robot on that data in a simulation environment and then apply it in the physical world. Armed with that knowledge and a webcam attached to its head, the robot was able to “follow” a human operator’s body and hand movements and eventually mimic them. This process, referred to as “shadowing” resulted in the humanoid robots replicating human movements more naturally. “By mimicking humans, humanoids can potentially tap into the rich repertoire of skills and motion exhibited by humans, offering a promising avenue towards achieving general robot intelligence,” the authors write. The various tasks and movements the robot was asked to mimic ran the gamut of human motion. In one example, the robot was tasked with putting on a shoe and walking, which tested both its hand dexterity and overall locomotion. Other tasks like playing ping pong or learning to throw a solid left jab, meanwhile, placed more of an emphasis on visual perception and timing. Another activity, which involved the robot using a keyboard to type the coding phrase “Hello Word” demonstrated more precise finger movements. Once fully trained, the researchers claim HumanPlus was successful in its movement 60-100% of the time, depending on the task. The Stanford researchers took a different approach. They first used a reinforcement AI model to train a custom designed robot, called “HumanPlus” on 40 hours of various human motion data. They could then take the base lessons learned by training the robot on that data in a simulation environment and then apply it in the physical world. Armed with that knowledge and a webcam attached to its head, the robot was able to “follow” a human operator’s body and hand movements and eventually mimic them. This process, referred to as “shadowing” resulted in the humanoid robots replicating human movements more naturally. “By mimicking humans, humanoids can potentially tap into the rich repertoire of skills and motion exhibited by humans, offering a promising avenue towards achieving general robot intelligence,” the authors write. The various tasks and movements the robot was asked to mimic ran the gamut of human motion. In one example, the robot was tasked with putting on a shoe and walking, which tested both its hand dexterity and overall locomotion. Other tasks like playing ping pong or learning to throw a solid left jab, meanwhile, placed more of an emphasis on visual perception and timing. Another activity, which involved the robot using a keyboard to type the coding phrase “Hello Word” demonstrated more precise finger movements. Once fully trained, the researchers claim HumanPlus was successful in its movement 60-100% of the time, depending on the task. The Stanford researchers took a different approach. They first used a reinforcement AI model to train a custom designed robot, called “HumanPlus” on 40 hours of various human motion data. They could then take the base lessons learned by training the robot on that data in a simulation environment and then apply it in the physical world. Armed with that knowledge and a webcam attached to its head, the robot was able to “follow” a human operator’s body and hand movements and eventually mimic them. This process, referred to as “shadowing” resulted in the humanoid robots replicating human movements more naturally. “By mimicking humans, humanoids can potentially tap into the rich repertoire of skills and motion exhibited by humans, offering a promising avenue towards achieving general robot intelligence,” the authors write. The various tasks and movements the robot was asked to mimic ran the gamut of human motion. In one example, the robot was tasked with putting on a shoe and walking, which tested both its hand dexterity and overall locomotion. Other tasks like playing ping pong or learning to throw a solid left jab, meanwhile, placed more of an emphasis on visual perception and timing. Another activity, which involved the robot using a keyboard to type the coding phrase “Hello Word” demonstrated more precise finger movements. Once fully trained, the researchers claim HumanPlus was successful in its movement 60-100% of the time, depending on the task.