Subgoal Diffuser: Coarse-to-fine Subgoal Generation to Guide Model Predictive Control for Robot Manipulation

Zixuan Huang, Yating Lin, Fan Yang, Dmitry Berenson

Abstract:

Long-horizon contact-rich manipulation has long
been a challenging problem, as it requires reasoning over
both discrete contact modes and continuous object motion.
We introduce Implicit Contact Diffuser (ICD), a diffusion-based
model that generates a sequence of neural descriptors that
specify a series of contact relationships between the object and
the environment. This sequence is then used as guidance for an
MPC method to accomplish a given task. The key advantage of
this approach is that the latent descriptors provide more taskrelevant guidance to MPC, helping to avoid local minima for
contact-rich manipulation tasks. Our experiments demonstrate
that ICD outperforms baselines on complex, long-horizon,
contact-rich manipulation tasks, such as cable routing and
notebook folding. Additionally, our experiments also indicate
that ICD can generalize a target contact relationship to a
different environment. More visualizations can be found on our
website https://implicit-contact-diffuser.github.io