Affordance detection of tool parts from geometric features

@article{Myers2015AffordanceDO,
  title={Affordance detection of tool parts from geometric features},
  author={Austin Oliver Myers and Ching Lik Teo and Cornelia Ferm{\"u}ller and Yiannis Aloimonos},
  journal={2015 IEEE International Conference on Robotics and Automation (ICRA)},
  year={2015},
  pages={1374-1381}
}
As robots begin to collaborate with humans in everyday workspaces, they will need to understand the functions of tools and their parts. To cut an apple or hammer a nail, robots need to not just know the tool's name, but they must localize the tool's parts and identify their functions. Intuitively, the geometry of a part is closely related to its possible functions, or its affordances. Therefore, we propose two approaches for learning affordances from local shape and geometry primitives: 1… CONTINUE READING

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