BiFold：语言引导下的双臂布料折叠
BiFold: Bimanual Cloth Folding with Language Guidance
AI总结：
针对布料折叠的复杂性与标注数据缺失问题，提出BiFold模型，借助预训练视觉语言模型实现语言引导的双臂折叠，构建新型数据集，在基准和新场景下均取得优异性能与强泛化性。
摘要：
布料折叠是一项复杂的任务，因为衣物不可避免地存在自遮挡，其动力学特性复杂，且服装的材质、几何形状和纹理千差万别。在本研究中，我们学习根据文本指令执行折叠动作。将高层抽象指令转化为精确的机器人动作，需要强大的语言理解与操作能力。为此，我们利用预训练视觉语言模型，将其改造用于预测操作动作。我们提出的BiFold模型能够结合上下文信息，在现有的语言条件折叠基准测试中取得了当前最优性能。为解决双臂折叠标注数据缺失的问题，我们构建了一个包含自动解析动作与语言对齐指令的新型数据集，助力更好地学习文本条件下的操作任务。BiFold在我们的数据集上表现最佳，并且在面对新指令、新衣物和新环境时展现出了强大的泛化能力。
英文摘要：
Cloth folding is a complex task due to the inevitable self-occlusions of clothes, their complicated dynamics, and the disparate materials, geometries, and textures that garments can have. In this work, we learn folding actions conditioned on text commands. Translating high-level, abstract instructions into precise robotic actions requires sophisticated language understanding and manipulation capabilities. To do that, we leverage a pre-trained vision-language model and repurpose it to predict manipulation actions. Our model, BiFold, can take context into account and achieves state-of-the-art performance on an existing language-conditioned folding benchmark. To address the lack of annotated bimanual folding data, we introduce a novel dataset with automatically parsed actions and language-aligned instructions, enabling better learning of text-conditioned manipulation. BiFold attains the best performance on our dataset and demonstrates strong generalization to new instructions, garments, and environments.
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。
