A robot operating in unstructured environments must be able to discriminate between different grasping styles depending on the prospective manipulation task. Having a system that allows learning from remote non-expert demonstrations can very feasibly extend the cognitive skills of a robot for task-oriented grasping. We propose a novel two-step framework towards this aim. The first step involves grasp area estimation by segmentation. We receive grasp area demonstrations for a new task via interactive segmentation, and learn from these few demonstrations to estimate the required grasp area on an unseen scene for the given task. The second step is autonomous grasp estimation in the segmented region. To train the segmentation network for few-shot learning, we built a grasp area segmentation (GAS) dataset with 10089 images grouped into 1121 segmentation tasks. We benefit from an efficient meta learning algorithm for training for few-shot adaptation. Experimental evaluation showed that our method successfully detects the correct grasp area on the respective objects in unseen test scenes and effectively allows remote teaching of new grasp strategies by non-experts.

翻译：在非结构化环境中运行的机器人必须能够根据预期操作任务区分不同的抓取方式。通过远程非专家演示进行学习的系统，能够切实扩展机器人面向任务抓取的认知能力。我们为此提出了一种新颖的两步框架。第一步通过分割进行抓取区域估计：我们通过交互式分割接收新任务的抓取区域演示，并从这些少量演示中学习，以估计给定任务在未见场景中所需的抓取区域。第二步是在分割区域内进行自主抓取估计。为训练用于小样本学习的分割网络，我们构建了一个抓取区域分割（GAS）数据集，包含10089张图像，分组为1121个分割任务。我们采用高效的元学习算法进行小样本自适应训练。实验评估表明，我们的方法能成功检测未见测试场景中相应物体上的正确抓取区域，并有效支持非专业人员远程教授新的抓取策略。