用扩散模型生成接触序列，指导机械臂完成复杂抓取任务
Implicit Contact Diffuser: Sequential Contact Reasoning with Latent Point Cloud Diffusion
- 用潜空间点云扩散生成接触关系序列
- 在长时序接触任务中成功率超基线，如排线、折纸
- 可跨环境泛化接触关系，适合复杂操作场景
长时序、高接触的操纵任务长期面临挑战，需同时推理离散接触模式与连续物体运动。我们提出隐式接触扩散器（ICD），一种基于扩散模型的方法，生成一系列神经描述符，用于刻画物体与环境之间的接触关系序列。该序列作为引导输入至MPC方法，以完成指定任务。该方法的核心优势在于，潜空间描述符能提供更任务相关的引导信息，帮助避免接触丰富任务中的局部最优陷阱。实验表明，ICD在复杂、长时序、高接触任务（如电缆布线和笔记本折叠）上优于基线方法。此外，实验还显示，该方法可将目标接触关系泛化至不同环境。更多可视化见官网：https://implicit-contact-diffuser.github.io/
原文摘要 · Abstract (English)
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 $\href{https://implicit-contact-diffuser.github.io/}{https://implicit-contact-diffuser.github.io}$
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