Learning to Generalize Across Long-Horizon Tasks from Human Demonstrations

Understand

Imitation learning is an effective and safe technique to train robot policies in the real world because it does not depend on an expensive random exploration process.

• However, due to the lack of exploration, learning policies that generalize beyond the demonstrated behaviors is still an open challenge.
• We present a novel imitation learning framework to enable robots to 1) learn complex real world manipulation tasks efficiently from a small number of human demonstrations, and 2) synthesize new behaviors not contained in the collected demonstrations.
• Our key insight is that multi-task domains often present a latent structure, where demonstrated trajectories for different tasks intersect at common regions of the state space.