Diffusion Policy with Bayesian Expert Selection for Active Multi-Target Tracking

ArXi:2604.03404v1 Announce Type: cross Active multi-target tracking requires a mobile robot to balance exploration for undetected targets with exploitation of uncertain tracked ones. Diffusion policies have emerged as a powerful approach for capturing diverse behavioral strategies by learning action sequences from expert nstrations. However, existing methods implicitly select among strategies through the denoising process, without uncertainty quantification over which strategy to execute.