Phys.org July 22, 2022
Researchers at Carnegie Mellon University have developed a new learning method for robots called WHIRL, short for In-the-Wild Human Imitating Robot Learning. WHIRL is an efficient algorithm for one-shot visual imitation. It can learn directly from human-interaction videos and generalize that information to new tasks, making robots well-suited to learning household chores. With WHIRL, a robot can observe those tasks and gather the video data it needs to eventually determine how to complete the job itself. The robot watched as a researcher opened the refrigerator door. It recorded his movements, the swing of the door, the location of the fridge and more, analyzing this data and readying itself to mimic the human. In the beginning it missed the handle completely, grabbed it in the wrong spot or pulled it incorrectly. But after a few hours of practice, the robot succeeded and opened the door. The team added a camera and their software to an off-the-shelf robot, and it learned how to do more than 20 tasks — from opening and closing cabinet doors and drawers to putting a lid on a pot, pushing in a chair, and even taking a garbage bag out of the bin. Each time, the robot watched a human complete the task once and then went about practicing and learning to accomplish the task on its own. Both reinforcement and WHIRL learning models work well. WHIRL can learn from any video of a human doing a task, it is easily scalable, not confined to one specific task and can operate in realistic environments…read more. Video