Abstract
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.
Abstract (translated)
在无组织环境下运行机器人必须能够根据未来的操作任务区分不同的抓握风格。拥有一个允许从远程非专家演示学习系统的系统可以非常有效地扩展机器人的任务导向抓握的认知技能。为此,我们提出了一个 novel 两个步骤的框架。第一步涉及分割领域的抓握面积估计。我们通过交互式分割获得一个新的任务的抓握面积演示,并从中学习,以估计针对给定任务在一个未访问的场景下的所需抓握面积。第二步是自动分割领域的抓握面积估计。为了训练分割网络进行少量学习,我们建立了一个包含10089张图像的抓握面积分割(GAS)数据集。我们利用高效的多任务学习算法进行少量学习的培训和适应。实验评估表明,我们的方法成功检测到在未访问的测试场景下相应的物体的正确抓握面积,并有效地允许非专家远程教授新的抓握策略。
URL
https://arxiv.org/abs/2303.10195
