Refining Grasp Affordance Models by Experience

Renaud Detry, Dirk Kraft, Anders Glent Buch, Norbert Krüger, Justus Piater

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


We present a method for learning object grasp affordance models in 3D from experience, and demonstrate its applicability through extensive testing and evaluation on a realistic and largely autonomous platform. Grasp affordance refers here to relative object-gripper configurations that yield stable grasps. These affordances are represented probabilistically with grasp densities, which correspond to continuous density functions defined on the space of 6D gripper poses. A grasp density characterizes an object’s grasp affordance; densities are linked to visual stimuli through registration with a visual model of the object they characterize. We explore a batch-oriented, experience-based learning paradigm where grasps sampled randomly from a density are performed, and an importance-sampling algorithm learns a refined density from the outcomes of these experiences. The first such learning cycle is bootstrapped with a grasp density formed from visual cues. We show that the robot effectively applies its experience by downweighting poor grasp solutions, which results in increased success rates at subsequent learning cycles. We also present success rates in a practical scenario where a robot needs to repeatedly grasp an object lying in an arbitrary pose, where each pose imposes a specific reaching constraint, and thus forces the robot to make use of the entire grasp density to select the most promising achievable grasp.
Original languageEnglish
Title of host publicationRobotics and Automation (ICRA), 2010 IEEE International Conference on
Number of pages7
Publication date15. Jul 2010
ISBN (Print)978-1-4244-5038-1
Publication statusPublished - 15. Jul 2010
EventIEEE International Conference on Robotics and Automation. ICRA'10 - Anchorage, Alaska, United States
Duration: 3. May 20108. May 2010


ConferenceIEEE International Conference on Robotics and Automation. ICRA'10
Country/TerritoryUnited States
CityAnchorage, Alaska


  • grippers
  • importance sampling
  • learning systems


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