Affordance detection of tool parts from geometric features

By Austin Myers, Ching Lik Teo, Cornelia Fermüller, Yiannis Aloimonos. IEEE International Conference on Robotics and Automation, 2015

341 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Control and Systems Engineering, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/0458cec30079a53a2b7726a14f5dd826b9b39bfd

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Citations in the corpus

SemiContour: A Semi-supervised Learning Approach for Contour Detection (2016, methodology): “Several variants of SRF are also successfully applied to image patch classification [29, 12, 25, 3, 22, 33].”
Rearrangement: A Challenge for Embodied AI (2020, background): ““is the bottle likely to break if it is dropped?” or “is the box light enough to be picked up or does it need to be pushed?”), or prediction of affordance heatmaps for objects [55].”
Joint Hand Motion and Interaction Hotspots Prediction from Egocentric Videos (2022, background): “Object affordance grounding [19,21,42,60,69,74] refers to locating where the interaction occurs on an object.”
AffordanceNet: An End-to-End Deep Learning Approach for Object Affordance Detection (2017, methodology): “For the UMD dataset, we also report the results from the geometric features-based approach (HMD and SRF) [4] and a deep learning-based approach that used both RGB and depth images as inputs (ED-RGBHHA) [14].”
Grounded Human-Object Interaction Hotspots From Video (2018, methodology): “tion: exemplars are often captured in artificial lab tabletop environments [36, 31, 48] and labeling cost naturally restricts the scope to a narrow set of objects.”
A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms (2019, methodology): “Representations at each level of the hierarchy can capture general inherent properties as well as semantic or task-oriented properties (Dang and Allen, 2012; Myers et al., 2015; Jang et al., 2017).”
Affordances from Human Videos as a Versatile Representation for Robotics (2023, neutral): “Given a scene, one can predict interactions using geometrybased rules for objects via 3D scene understanding [43, 78, 79, 134], estimating 3D physical attributes [8, 26, 41, 137] or through segmentation models trained on semantic interactions [102, 104], and thus require specialized datasets.”
Learning Dexterous Grasping with Object-Centric Visual Affordances (2020, background): “Traditionally, supervision comes from labeled image examples [32], [9] or a robot’s grasp success/failure [40], [22], [23], while newer work explores weaker modes of supervision from”
Tool Macgyvering: Tool Construction Using Geometric Reasoning (2019, background): “1Most tools have two key components, grasp part and action part [22], [23]”