Zero-shot Imitation Learning by Latent Topology Mapping

TL;DR

ZALT is a novel imitation learning approach that leverages latent hub states and topology planning to enable zero-shot adaptation to unseen long-horizon tasks in complex environments.

Contribution

The paper introduces ZALT, a method that identifies latent hub states and plans over their topology, enabling zero-shot generalization beyond demonstrated tasks.

Findings

ZALT achieves 55% zero-shot success on unseen tasks in a 3D maze.

Compared to the baseline, ZALT significantly improves zero-shot adaptation performance.

The topology-based approach enables composability and abstraction of long-horizon behaviors.

Abstract

Imitation learning is effective for training agents when expert demonstrations are available, but collecting demonstrations for every complex task in an environment is costly. We study the long-horizon, goal-conditioned setting where a fixed demonstration dataset contains useful behavior, but not complete examples for every task the agent must solve. Existing imitation learning methods can learn strong policies from demonstrations, but when solving long-horizon tasks, small errors accumulate over long primitive-action trajectories and make zero-shot adaptation to new tasks unreliable. We introduce Zero-shot Agents from Latent Topologies (ZALT), an imitation-learning method that solves unseen start-goal tasks beyond those demonstrated during training. ZALT identifies latent hub states where trajectories converge or diverge, learns policies and a dynamics model over hub-to-hub transitions, and plans over the hub topology to complete new tasks. This topology makes demonstrated behaviors explicitly composable while compressing long tasks into shorter sequences of abstract transitions -- combined, these enable ZALT to perform zero-shot adaptation. In a complex 3D maze environment, ZALT achieves 55% zero-shot success on unseen tasks, compared to 6% for the strongest baseline.