# Grasp-type Recognition Leveraging Object Affordance

arXiv: https://arxiv.org/abs/2009.09813 · HTML: https://mlsanity.com/paper/2009.09813/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Aug 2020.

## Built on by (most cited first, 1 of 1)

- [Text-driven object affordance for guiding grasp-type recognition in multimodal robot teaching](https://mlsanity.com/paper/2103.00268/) (2021)

## Abstract (arXiv)

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.