Grasp-type Recognition Leveraging Object Affordance

Understand

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.