面向双手抓取规划的物理感知显著性图迭代学习与预测
Physics-Aware Iterative Learning and Prediction of Saliency Map for Bimanual Grasp Planning
AI总结：
针对双手抓取规划搜索空间大、标注数据需求多的问题，提出基于单手抓取数据的双手抓取显著性学习框架，结合物理平衡损失与优化模块，高效实现平衡的双手抓取。
摘要：
学习人类双手抓取技能可拓展机器人系统抓取大型或重型物体的能力。但双手抓取的抓取点搜索空间远大于单手抓取，且网络学习需要大量双手抓取标注，导致数据驱动或解析型抓取方法效率低下且能力不足。我们提出一种双手抓取显著性学习框架，旨在基于现有的人类单手抓取数据预测双手抓取的接触点。我们通过最少的双手接触标注学习显著性对应向量，建立双手抓取位置间的对应关系，无需训练大规模双手抓取数据集。现有单手抓取显著性值作为双手抓取显著性的初始值，我们学习一个显著性调整分数，将其与初始值相加得到最终的双手抓取显著性值，可从单手抓取显著性中预测优选的双手抓取位置。我们还引入了物理平衡损失函数和物理感知优化模块，实现抓取的物理平衡，提升对未知物体的泛化能力。仿真环境中的综合实验及在灵巧夹爪上的对比实验表明，我们的方法可有效实现平衡的双手抓取。
英文摘要：
Learning the skill of human bimanual grasping can extend the capabilities of robotic systems when grasping large or heavy objects. However, it requires a much larger search space for grasp points than single-hand grasping and numerous bimanual grasping annotations for network learning, making both data-driven or analytical grasping methods inefficient and insufficient. We propose a framework for bimanual grasp saliency learning that aims to predict the contact points for bimanual grasping based on existing human single-handed grasping data. We learn saliency corresponding vectors through minimal bimanual contact annotations that establishes correspondences between grasp positions of both hands, capable of eliminating the need for training a large-scale bimanual grasp dataset. The existing single-handed grasp saliency value serves as the initial value for bimanual grasp saliency, and we learn a saliency adjusted score that adds the initial value to obtain the final bimanual grasp saliency value, capable of predicting preferred bimanual grasp positions from single-handed grasp saliency. We also introduce a physics-balance loss function and a physics-aware refinement module that enables physical grasp balance, capable of enhancing the generalization of unknown objects. Comprehensive experiments in simulation and comparisons on dexterous grippers have demonstrated that our method can achieve balanced bimanual grasping effectively.
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。
