Generative Trajectory Modelling through Masked Diffusion

Behavioural cloning aims to imitate expert behaviour through supervision: a model is trained to predict the expert's actions from the observations it encountered. However, this framing discards the generative structure of trajectories, which arise from an interplay between the expert's policy and the environment's latent dynamics. Generative modelling is therefore a more natural fit. We motivate this perspective through four benefits: richer auxiliary learning signals, improved generalisation, temporal coherence, and robustness to unseen dynamics. Building on this, we propose a method for generative trajectory modelling that combines diffusion models with masked modelling, framing future trajectory prediction as a trajectory “inpainting” problem. We find this approach performs best under extremely high masking ratios of over 95%, reflecting the temporal redundancy in trajectories. Across a range of tasks, our method outperforms existing alternatives in efficiency (steps to task completion), accuracy (lower deviation from expert actions, both in-distribution and out-of-distribution), and generalisation to novel dynamics. To our knowledge, this is also the first demonstration that masked modelling can be effectively combined with denoising diffusion models.