  Training general robotic policies from heterogeneous data for different tasks
is a significant challenge. Existing robotic datasets vary in different
modalities such as color, depth, tactile, and proprioceptive information, and
collected in different domains such as simulation, real robots, and human
videos. 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. In this work, we present a flexible
approach, dubbed 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. Our method can use task-level composition for multi-task
manipulation and be composed with analytic cost functions to adapt policy
behaviors at inference time. We train our method on simulation, human, and real
robot data and evaluate in tool-use tasks. The composed policy achieves robust
and dexterous performance under varying scenes and tasks and outperforms
baselines from a single data source in both simulation and real-world
experiments. See https://liruiw.github.io/policycomp for more details .
