面向多本体抓取的扩散方法
Diffusion for Multi-Embodiment Grasping
AI总结：
针对抓取方法难以适配不同夹爪的问题，本文提出等变扩散模型，结合夹爪几何实现夹爪无关场景编码与夹爪感知位姿解码，并构建杂乱场景数据集生成框架，在多类夹爪上取得优于现有方法的泛化性能。
摘要：
抓取是机器人学中的一项基础技能，在医疗、工业和家庭领域具有广泛应用。然而，当前用于预测有效抓取的方法通常针对特定夹爪定制，当夹爪设计发生变化时，其适用性会受到限制。为解决这一局限，我们探索在不同夹爪设计之间迁移抓取策略，从而能够利用来自多种来源的数据。在本研究中，我们提出一种基于等变扩散的方法，通过将夹爪几何结构整合到模型中，实现对包含可抓取物体的场景的夹爪无关编码，以及对抓取位姿的夹爪感知解码。我们还开发了一个数据集生成框架，用于生成具有不同尺寸物体堆的杂乱场景，以改进抓取合成方法的训练。在多种物体数据集上的实验评估表明，我们的方法能够在从简单的平行爪夹爪到仿人手的各种夹爪架构之间实现泛化，并且性能优于单夹爪和多夹爪的现有最先进方法。
英文摘要：
Grasping is a fundamental skill in robotics with diverse applications across medical, industrial, and domestic domains. However, current approaches for predicting valid grasps are often tailored to specific grippers, limiting their applicability when gripper designs change. To address this limitation, we explore the transfer of grasping strategies between various gripper designs, enabling the use of data from diverse sources. In this work, we present an approach based on equivariant diffusion that facilitates gripper-agnostic encoding of scenes containing graspable objects and gripper-aware decoding of grasp poses by integrating gripper geometry into the model. We also develop a dataset generation framework that produces cluttered scenes with variable-sized object heaps, improving the training of grasp synthesis methods. Experimental evaluation on diverse object datasets demonstrates the generalizability of our approach across gripper architectures, ranging from simple parallel-jaw grippers to humanoid hands, outperforming both single-gripper and multi-gripper state-of-the-art methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
