# Text-driven object affordance for guiding grasp-type recognition in multimodal robot teaching

arXiv: https://arxiv.org/abs/2103.00268 · HTML: https://mlsanity.com/paper/2103.00268/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 60 references that are in the map.
- First posted on arXiv 27 Feb 2021.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning joint reconstruction of hands and manipulated objects](https://mlsanity.com/paper/1904.05767/) (2019)
- [First-Person Hand Action Benchmark with RGB-D Videos and 3D Hand Pose Annotations](https://mlsanity.com/paper/1704.02463/) (2017)
- [ContactDB: Analyzing and Predicting Grasp Contact via Thermal Imaging](https://mlsanity.com/paper/1904.06830/) (2019)
- [Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping](https://mlsanity.com/paper/1709.07857/) (2017)
- [AffordanceNet: An End-to-End Deep Learning Approach for Object Affordance Detection](https://mlsanity.com/paper/1709.07326/) (2017)
- [Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approach](https://mlsanity.com/paper/1804.05172/) (2018)
- [Deep Learning for Detecting Robotic Grasps](https://mlsanity.com/paper/1301.3592/) (2013)
- [Data-Driven Grasp Synthesis - A Survey](https://mlsanity.com/paper/1309.2660/) (2013)
- [Real-Time Grasp Detection Using Convolutional Neural Networks](https://mlsanity.com/paper/1412.3128/) (2014)
- [Learning Task-Oriented Grasping for Tool Manipulation from Simulated Self-Supervision](https://mlsanity.com/paper/1806.09266/) (2018)
- [Learning Task-Oriented Grasping from Human Activity Datasets](https://mlsanity.com/paper/1910.11669/) (2019)
- [Learning to Estimate Pose and Shape of Hand-Held Objects from RGB Images](https://mlsanity.com/paper/1903.03340/) (2019)
- [A Learning-from-Observation Framework: One-Shot Robot Teaching for Grasp-Manipulation-Release Household Operations](https://mlsanity.com/paper/2008.01513/) (2020)
- [Understanding hand-object manipulation by modeling the contextual relationship between actions, grasp types and object attributes](https://mlsanity.com/paper/1807.08254/) (2018)
- [Verbal Focus-of-Attention System for Learning-from-Observation](https://mlsanity.com/paper/2007.08705/) (2020)
- [Task-oriented Motion Mapping on Robots of Various Configuration using Body Role Division](https://mlsanity.com/paper/2007.08750/) (2020)
- [Grasp-type Recognition Leveraging Object Affordance](https://mlsanity.com/paper/2009.09813/) (2020)

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

- [GPT-4V(ision) for Robotics: Multimodal Task Planning from Human Demonstration](https://mlsanity.com/paper/2311.12015/) (2023)

## Abstract (arXiv)

This study investigates how text-driven object affordance, which provides prior knowledge about grasp types for each object, affects image-based grasp-type recognition in robot teaching. The researchers created labeled datasets of first-person hand images to examine the impact of object affordance on recognition performance. They evaluated scenarios with real and illusory objects, considering mixed reality teaching conditions where visual object information may be limited. The results demonstrate that object affordance improves image-based recognition by filtering out unlikely grasp types and emphasizing likely ones. The effectiveness of object affordance was more pronounced when there was a stronger bias towards specific grasp types for each object. These findings highlight the significance of object affordance in multimodal robot teaching, regardless of whether real objects are present in the images. Sample code is available on https://github.com/microsoft/arr-grasp-type-recognition.