We evaluate our approach extensively and demonstrate that we successfully learn novel tasks and generate corresponding agent plans and motion in (1) unseen configurations and (2) in composition with training tasks.

Learning new tasks with imitation learning often requires hundreds of demos. Check out our #NeurIPS paper in which we learn new tasks from few demos by inverting them into the latent space of a generative model pre-trained on a set of base tasks.

avivne.github.io/ftl-igm/