Implicit Contact Diffuser: Sequential Contact Reasoning with Latent Point Cloud Diffusion
This addresses complex manipulation problems in robotics, but appears incremental as it builds on diffusion-based models and MPC for specific tasks.
The paper tackles long-horizon contact-rich manipulation tasks, such as cable routing and notebook folding, by introducing Implicit Contact Diffuser (ICD), which generates neural descriptors to guide MPC, outperforming baselines and showing generalization to different environments.
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 task-relevant 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 \methodshort can generalize a target contact relationship to a different environment. More visualizations can be found on our website $\href{https://implicit-contact-diffuser.github.io/}{https://implicit-contact-diffuser.github.io}$