基于属性的机器人抓取与数据高效适配
Attribute-Based Robotic Grasping with Data-Efficient Adaptation
AI总结：
本文提出基于属性的端到端编码-解码抓取网络，并结合对抗适配与单次抓取适配，使机器人在仿真自监督预训练后高效泛化到未知物体，真实抓取成功率超过81%。
摘要：
机器人抓取是最基础的机器人操作任务之一，并且一直是广泛研究的主题。然而，在杂乱环境中快速教会机器人抓取新颖目标物体仍然具有挑战性。本文尝试利用有助于识别、抓取以及向新领域快速适配的物体属性来应对这一挑战。在本研究中，我们提出一种端到端编码器-解码器网络，用于学习基于属性的机器人抓取，并具备数据高效适配能力。我们首先使用多种基础物体对该端到端模型进行预训练，以学习用于识别和抓取的通用属性表示。我们的方法使用门控注意力机制融合工作空间图像与查询文本的嵌入，并学习预测实例抓取可供性。为了训练视觉属性和文本属性的联合嵌入空间，机器人利用抓取前后的物体持久性。我们的模型在仅使用各种颜色和形状的基础物体的仿真环境中进行自监督训练，但能够泛化到新环境中的新颖物体。为进一步促进泛化，我们提出两种适配方法：对抗适配和单次抓取适配。对抗适配使用无标签图像的增强数据调节图像编码器，而单次抓取适配利用来自一次抓取试验的增强数据更新整个端到端模型。两种适配方法都具有数据高效性，并显著提升实例抓取性能。仿真和真实世界中的实验结果表明，我们的方法在未知物体上实现了超过81%的实例抓取成功率，大幅优于若干基线方法。
英文摘要：
Robotic grasping is one of the most fundamental robotic manipulation tasks and has been the subject of extensive research. However, swiftly teaching a robot to grasp a novel target object in clutter remains challenging. This paper attempts to address the challenge by leveraging object attributes that facilitate recognition, grasping, and rapid adaptation to new domains. In this work, we present an end-to-end encoder-decoder network to learn attribute-based robotic grasping with data-efficient adaptation capability. We first pre-train the end-to-end model with a variety of basic objects to learn generic attribute representation for recognition and grasping. Our approach fuses the embeddings of a workspace image and a query text using a gated-attention mechanism and learns to predict instance grasping affordances. To train the joint embedding space of visual and textual attributes, the robot utilizes object persistence before and after grasping. Our model is self-supervised in a simulation that only uses basic objects of various colors and shapes but generalizes to novel objects in new environments. To further facilitate generalization, we propose two adaptation methods, adversarial adaption and one-grasp adaptation. Adversarial adaptation regulates the image encoder using augmented data of unlabeled images, whereas one-grasp adaptation updates the overall end-to-end model using augmented data from one grasp trial. Both adaptation methods are data-efficient and considerably improve instance grasping performance. Experimental results in both simulation and the real world demonstrate that our approach achieves over 81% instance grasping success rate on unknown objects, which outperforms several baselines by large margins.
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。
