Zero-shot Imitation Learning by Latent Topology Mapping
Researchers Maxwell J. Jacobson and Yexiang Xue have introduced a new imitation learning method called Zero-shot Agents from Latent Topologies (ZALT), detailed in a paper submitted to arXiv on May 8, 2026. Traditional imitation learning struggles with long-horizon tasks because small errors accumulate over time, making zero-shot adaptation to unseen tasks unreliable. ZALT addresses this by identifying latent hub states where trajectories converge or diverge. It learns policies and dynamics models specifically for transitions between these hubs, allowing the agent to plan over a compressed topology of abstract transitions rather than primitive actions. This approach makes demonstrated behaviors explicitly composable. In tests within a complex 3D maze environment, ZALT achieved a 55% success rate on unseen start-goal tasks, significantly outperforming the strongest baseline method, which only achieved a 6% success rate. This breakthrough suggests a viable path for training agents to solve complex, goal-conditioned tasks without requiring exhaustive demonstration datasets for every possible scenario, thereby reducing the cost and effort associated with collecting expert data.
Wire timeline
Zero-shot Imitation Learning by Latent Topology Mapping
Researchers Maxwell J. Jacobson and Yexiang Xue have introduced a new imitation learning method called Zero-shot Agents from Latent Topologies (ZALT), detailed in a paper submitted to arXiv on May 8, 2026. Traditional imitation learning struggles with long-horizon tasks because small errors accumulate over time, making zero-shot adaptation to unseen tasks unreliable. ZALT addresses this by identifying latent hub states where trajectories converge or diverge. It learns policies and dynamics models specifically for transitions between these hubs, allowing the agent to plan over a compressed topology of abstract transitions rather than primitive actions. This approach makes demonstrated behaviors explicitly composable. In tests within a complex 3D maze environment, ZALT achieved a 55% success rate on unseen start-goal tasks, significantly outperforming the strongest baseline method, which only achieved a 6% success rate. This breakthrough suggests a viable path for training agents to solve complex, goal-conditioned tasks without requiring exhaustive demonstration datasets for every possible scenario, thereby reducing the cost and effort associated with collecting expert data.
cs.AI updates on arXiv.org