  The fundamental challenge of planning for multi-step manipulation is to find
effective and plausible action sequences that lead to the task goal. We present
Cascaded Variational Inference (CAVIN) Planner, a model-based method that
hierarchically generates plans by sampling from latent spaces. To facilitate
planning over long time horizons, our method learns latent representations that
decouple the prediction of high-level effects from the generation of low-level
motions through cascaded variational inference. This enables us to model
dynamics at two different levels of temporal resolutions for hierarchical
planning. We evaluate our approach in three multi-step robotic manipulation
tasks in cluttered tabletop environments given high-dimensional observations.
Empirical results demonstrate that the proposed method outperforms
state-of-the-art model-based methods by strategically interacting with multiple
objects.
