Implicit Contact Diffuser: Sequential Contact Reasoning with Latent Point Cloud Diffusion
Huang, Zixuan, He, Yinong, Lin, Yating, Berenson, Dmitry
–arXiv.org Artificial Intelligence
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 $\href{https://implicit-contact-diffuser.github.io/}{https://implicit-contact-diffuser.github.io}$
arXiv.org Artificial Intelligence
Oct-21-2024
- Country:
- North America > United States (0.28)
- Genre:
- Research Report (0.82)
- Technology:
- Information Technology > Artificial Intelligence
- Machine Learning > Neural Networks (0.68)
- Representation & Reasoning (1.00)
- Robots > Manipulation (0.46)
- Information Technology > Artificial Intelligence