Learning Visual Object Categories for Robot Affordance Prediction

By Jie Sun, Joshua L. Moore, Aaron F. Bobick, James M. Rehg. 2010

94 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Control and Systems Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Thomas A. Funkhouser
2. Hema Swetha Koppula
3. Leonidas J. Guibas
4. Rudhir Gupta
5. Vladimir G. Kim
6. Ashutosh Saxena
7. James M. Rehg
8. Siddhartha Chaudhuri

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Jitendra Malik
3. Kristen Grauman
4. Dacheng Tao
5. Trevor Darrell
6. Ming-Hsuan Yang
7. Li Fei-Fei
8. Xiaogang Wang

In Control and Systems Engineering

1. Ashutosh Saxena
2. P. Hanrahan
3. Hema Swetha Koppula
4. Leonidas J. Guibas
5. Rudhir Gupta
6. Thomas A. Funkhouser
7. Siddhartha Chaudhuri
8. Vladimir G. Kim

Citations in the corpus

• Learning human activities and object affordances from RGB-D videos (2012, background): “For instance, Sun et al. (2009) proposed a probabilistic graphical model that leverages visual object categorization for learning affordances and Hermans et al. (2011) proposed the use of physical and visual attributes as a mid-level representation for affordance prediction.”
• Anticipating Human Activities Using Object Affordances for Reactive Robotic Response (2013, background): “These works take a recognition based approach to identify the semantic affordance labels [70], [71], [72].”
• SceneGrok (2014, background): “There is a variety of related work that has looked at using human-object interactions to improve pose estimation, object recognition, action classification, and other related problem in different communities [Fritz et al. 2006; Montesano et al. 2008; Stark et al. 2008; Sun et al. 2010; Hermans et al”
• Shape2Pose (2014, methodology): “2011], note that this analysis is also commonly performed jointly with object classification [Sun et al. 2009; Stark et al. 2008].”
• Affordance Prediction via Learned Object Attributes (2011, methodology): “We call this method direct perception (DP) of affordances, following the convention of Gibson and others [2, 13].”