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Learning Action-oriented grasping for manipulation

Author
Ud Din, M.; Sarwar, M. Usman; Zahoor, I.; Qazi, W.; Rosell, J.
Type of activity
Presentation of work at congresses
Name of edition
24th IEEE International Conference on Emerging Technologies and Factory Automation
Date of publication
2019
Presentation's date
2019-09-13
Book of congress proceedings
Proceedings 2019 24th IEEE International Conference on Emerging Technologies and Factory Automation
First page
1575
Last page
1578
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Project funding
Autonomous dexterous robots as coworkers with human operators
Repository
http://hdl.handle.net/2117/168677 Open in new window
https://ioc.upc.edu/ca/personal/jan.rosell/publications/papers/2019_etfa_learning_action-oriented_grasping_for_manipulation_muhayyudinetal.pdf/view Open in new window
Abstract
Complex manipulation tasks require grasping strategies that simultaneously satisfy the stability and the semantic constraints that have to be satisfied for an action to be feasible, referred as action-oriented semantic grasp strategies. This study develops a framework using machine learning techniques to compute action-oriented semantic grasps. It takes a 3D model of the object and the action to be performed as input and provides a vector of action-oriented semantic grasps. We evaluate the perfo...
Citation
Ud Din, M. [et al.]. Learning Action-oriented grasping for manipulation. A: IEEE International Conference on Emerging Technologies and Factory Automation. "Proceedings 2019 24th IEEE International Conference on Emerging Technologies and Factory Automation". Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 1575-1578.
Group of research
SIR - Service and Industrial Robotics

Participants

  • Ud Din, Muhayy  (author and speaker )
  • Sarwar, M Usman  (author and speaker )
  • Zahoor, Imran  (author and speaker )
  • Qazi, Wajahat M  (author and speaker )
  • Rosell Gratacos, Joan  (author and speaker )