论文标题
掌握型识别利用物体负担
Grasp-type Recognition Leveraging Object Affordance
论文作者
论文摘要
机器人教学中的一个关键挑战是具有单个RGB图像和目标对象名称的Grasp-Type识别。在这里，我们提出了一条简单而有效的管道，通过利用每个对象的掌握类型的先前分布来增强基于学习的识别。在管道中，卷积神经网络（CNN）从RGB图像中识别GRASP类型。使用先前的分布（即负担）进一步纠正识别结果，该分布与目标对象名称相关联。实验结果表明，该提出的方法的表现均优于仅CNN和仅负担能力的方法。结果突出了语言驱动的物体负担能力在机器人教学中提高掌握型识别的有效性。
A key challenge in robot teaching is grasp-type recognition with a single RGB image and a target object name. Here, we propose a simple yet effective pipeline to enhance learning-based recognition by leveraging a prior distribution of grasp types for each object. In the pipeline, a convolutional neural network (CNN) recognizes the grasp type from an RGB image. The recognition result is further corrected using the prior distribution (i.e., affordance), which is associated with the target object name. Experimental results showed that the proposed method outperforms both a CNN-only and an affordance-only method. The results highlight the effectiveness of linguistically-driven object affordance for enhancing grasp-type recognition in robot teaching.
