Towards Generalisable Imitation Learning Through Conditioned Transition Estimation and Online Behaviour Alignment

TY - CHAP

T1 - Towards Generalisable Imitation Learning Through Conditioned Transition Estimation and Online Behaviour Alignment

AU - Schneider Gavenski, Gavenski

AU - Leonetti, Matteo

AU - Rodrigues, Odinaldo

N1 - Conference code: 25

PY - 2026/1/21

Y1 - 2026/1/21

N2 - State-of-the-art imitation learning from observation methods (ILfO) have recently made significant progress, but they still have some limitations: they need action-based supervised optimisation, assume that states have a single optimal action, and tend to apply teacher actions without full consideration of the actual environment state. While the truth may be out there in observed trajectories, existing methods struggle to extract it without supervision. In this work, we propose Unsupervised Imitation Learning from Observation (UfO) that addresses all of these limitations. UfO learns a policy through a two-stage process, in which the agent first obtains an approximation of the teacher’s true actions in the observed state transitions, and then refines the learned policy further by adjusting agent trajectories to closely align them with the teacher’s. Experiments we conducted in five widely used environments show that UfO not only outperforms the teacher and all other ILfO methods but also displays the smallest standard deviation. This reduction in standard deviation indicates better generalisation in unseen scenarios.

AB - State-of-the-art imitation learning from observation methods (ILfO) have recently made significant progress, but they still have some limitations: they need action-based supervised optimisation, assume that states have a single optimal action, and tend to apply teacher actions without full consideration of the actual environment state. While the truth may be out there in observed trajectories, existing methods struggle to extract it without supervision. In this work, we propose Unsupervised Imitation Learning from Observation (UfO) that addresses all of these limitations. UfO learns a policy through a two-stage process, in which the agent first obtains an approximation of the teacher’s true actions in the observed state transitions, and then refines the learned policy further by adjusting agent trajectories to closely align them with the teacher’s. Experiments we conducted in five widely used environments show that UfO not only outperforms the teacher and all other ILfO methods but also displays the smallest standard deviation. This reduction in standard deviation indicates better generalisation in unseen scenarios.

KW - Imitation Learning

KW - Generalisation

M3 - Conference paper

BT - The 25th International Conference on Autonomous Agents and Multiagent Systems

T2 - 25th International Conference on Autonomous Agents and Multiagent Systems

Y2 - 25 May 2026 through 29 May 2026

ER -