  Task and Motion Planning (TAMP) approaches are effective at planning
long-horizon autonomous robot manipulation. However, it can be difficult to
apply them to domains where the environment and its dynamics are not fully
known. We propose to overcome these limitations by leveraging deep generative
modeling, specifically diffusion models, to learn constraints and samplers that
capture these difficult-to-engineer aspects of the planning model. These
learned samplers are composed and combined within a TAMP solver in order to
find action parameter values jointly that satisfy the constraints along a plan.
To tractably make predictions for unseen objects in the environment, we define
these samplers on low-dimensional learned latent embeddings of changing object
state. We evaluate our approach in an articulated object manipulation domain
and show how the combination of classical TAMP, generative learning, and latent
embeddings enables long-horizon constraint-based reasoning. We also apply the
learned sampler in the real world. More details are available at
https://sites.google.com/view/dimsam-tamp
