Implicit Contact Diffuser: Sequential Contact Reasoning with Latent Point Cloud Diffusion
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909358875475968 |
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| author | Huang, Zixuan He, Yinong Lin, Yating Berenson, Dmitry |
| author_facet | Huang, Zixuan He, Yinong Lin, Yating Berenson, Dmitry |
| contents | 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 $\href{https://implicit-contact-diffuser.github.io/}{https://implicit-contact-diffuser.github.io}$ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_16571 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Implicit Contact Diffuser: Sequential Contact Reasoning with Latent Point Cloud Diffusion Huang, Zixuan He, Yinong Lin, Yating Berenson, Dmitry Robotics Artificial Intelligence Machine Learning 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 $\href{https://implicit-contact-diffuser.github.io/}{https://implicit-contact-diffuser.github.io}$ |
| title | Implicit Contact Diffuser: Sequential Contact Reasoning with Latent Point Cloud Diffusion |
| topic | Robotics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2410.16571 |