The paper discusses the challenge of training general robotic policies from heterogeneous data for various tasks. Current methods often use data from one domain, which is both expensive and difficult….
Continue readingThe article introduces an innovative architecture to estimate causal effects from data spread across different silos. The architecture’s design allows for the seamless transmission of model parameters enriched with causal…
Continue readingThe article presents AnyD, a geographically-aware conditional imitation learning model for autonomous driving. AnyD can efficiently learn from diverse and globally distributed data, including dynamic environmental, traffic, and social characteristics….
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