Short-horizon tasks require few steps: e.g., go to a tree and chop it down, dig a hole.

In Table 1, we report the cross-task testbed results in two transfer learning settings, i.e., (a) node classification to link prediction (Table 1a) and (b) link prediction to node classification (Table 1b).

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OpenSTL provides a modular and extensible framework implementing various state-of-the-art methods. We conduct standard evaluations on datasets across various domains, including synthetic moving object trajectory, human motion, driving scenes, traffic flow, and weather forecasting.

We then resample a dataset with these domain weights and train a larger, full-sized model.