MIT News June 3, 2024 Existing robotic datasets vary in different modalities such as color, depth, tactile, and proprioceptive information, and collected in different domains. Current methods usually collect and pool all data from one domain to train a single policy to handle such heterogeneity in tasks and domains, which is prohibitively expensive and difficult. Researchers at MIT presented a flexible approach, called Policy Composition, to combine information across such diverse modalities and domains for learning scene-level and task-level generalized manipulation skills, by composing different data distributions represented with diffusion models. Their method could use task-level composition for multi-task manipulation […]
Tag Archives: Training robots
Robots learn household tasks by watching humans
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 […]