Template-based learning of grasp selection


Conference Paper



The ability to grasp unknown objects is an important skill for personal robots, which has been addressed by many present and past research projects, but still remains an open problem. A crucial aspect of grasping is choosing an appropriate grasp configuration, i.e. the 6d pose of the hand relative to the object and its finger configuration. Finding feasible grasp configurations for novel objects, however, is challenging because of the huge variety in shape and size of these objects. Moreover, possible configurations also depend on the specific kinematics of the robotic arm and hand in use. In this paper, we introduce a new grasp selection algorithm able to find object grasp poses based on previously demonstrated grasps. Assuming that objects with similar shapes can be grasped in a similar way, we associate to each demonstrated grasp a grasp template. The template is a local shape descriptor for a possible grasp pose and is constructed using 3d information from depth sensors. For each new object to grasp, the algorithm then finds the best grasp candidate in the library of templates. The grasp selection is also able to improve over time using the information of previous grasp attempts to adapt the ranking of the templates. We tested the algorithm on two different platforms, the Willow Garage PR2 and the Barrett WAM arm which have very different hands. Our results show that the algorithm is able to find good grasp configurations for a large set of objects from a relatively small set of demonstrations, and does indeed improve its performance over time. View full abstract

Author(s): Alexander Herzog and Peter Pastor and Mrinal Kalakrishnan and Ludovic Righetti and Tamim Asfour and Stefan Schaal
Book Title: IEEE International Conference on Robotics and Automation (ICRA)
Pages: 2379-2384
Year: 2012
Month: May

Department(s): Autonomous Motion, Movement Generation and Control
Research Project(s): Template-Based Learning of Model Free Grasping
Bibtex Type: Conference Paper (inproceedings)

DOI: 10.1109/ICRA.2012.6225271

Links: video
Attachments: pdf


  title = {Template-based learning of grasp selection},
  author = {Herzog, Alexander and Pastor, Peter and Kalakrishnan, Mrinal and Righetti, Ludovic and Asfour, Tamim and Schaal, Stefan},
  booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
  pages = {2379-2384},
  month = may,
  year = {2012},
  month_numeric = {5}