Dynamics Learning with Cascaded Variational Inference for Multi-Step Manipulation

Understand

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.