Humanoid Policy ~ Human Policy
Apple Machine Learning Research
Training manipulation policies for humanoid robots with
diverse data enhances their robustness and generalization across tasks and platforms. However, learning solely from robot demonstrations is labor-intensive, requiring expensive tele-operated data
collection which is difficult to scale. This paper investigates a more scalable data source, egocentric human demonstrations, to serve as cross-embodiment training data for robot learning. We mitigate the embodiment gap between humanoids and humans
from both the data and modeling perspectives. We collect an egocentric task-oriented dataset (PH2D)…
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21/05/2025 – 21:05 /
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