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 […]