Noise-Gated Learning from Prior Experience for Generative Control

Learning generative policies via behavior cloning demands high-quality, task-specific demonstrations that can be time-consuming to collect. Prior experience, including policy rollouts and data collected on other tasks, is more readily available and contains useful information such as environment dynamics, but can harm task performance if naively imitated. We introduce Noise-Gated Diffuser (NGD), a generative control framework that selectively draws supervision from prior experience beyond limited expert demonstrations. NGD jointly models actions and a compact future-state latent with independent noise levels. These define a two-dimensional noise plane that connects behavior imitation and dynamics modeling through a continuum of learning objectives. We use noise thresholds to select which regions receive supervision from each data source, with a learned embedding making the source identity explicit to the model. Across simulated and real-robot manipulation tasks with limited expert demonstrations, NGD consistently benefits from prior experience, with intermediate noise thresholds yielding the strongest average performance. On the real-robot tasks, NGD achieves nearly four times the average success rate of expert-only Diffusion Policy. Moreover, with expert demonstrations held fixed, the performance of \ours increases linearly with the amount of prior experience, highlighting its promise for scaling generative control with diverse trajectory collections.