  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.
