Learning grasp stability with tactile data and HMMs
Paper in proceeding, 2010

In this paper, the problem of learning grasp stability in robotic object grasping based on tactile measurements is studied. Although grasp stability modeling and estimation has been studied for a long time, there are few robots today able of demonstrating extensive grasping skills. The main contribution of the work presented here is an investigation of probabilistic modeling for inferring grasp stability based on learning from examples. The main objective is classification of a grasp as stable or unstable before applying further actions on it, e.g. lifting. The problem cannot be solved by visual sensing which is typically used to execute an initial robot hand positioning with respect to the object. The output of the classification system can trigger a regrasping step if an unstable grasp is identified. An off-line learning process is implemented and used for reasoning about grasp stability for a three-fingered robotic hand using Hidden Markov models. To evaluate the proposed method, experiments are performed both in simulation and on a real robot system.

Grasping

Tactile sensors

Shape

Hidden Markov models

Stability analysis

Author

Yasemin Bekiroglu

Royal Institute of Technology (KTH)

Danica Kragic

Royal Institute of Technology (KTH)

Ville Kyrki

Lappeenranta-Lahti University of Technology (LUT)

IEEE International Symposium on Robot and Human Interactive Communication

1944-9445 (ISSN) 1944-9437 (eISSN)

132-137

IEEE International Symposium on Robot and Human Interactive Communication
Viareggio, Italy,

Subject Categories

Robotics

Computer Vision and Robotics (Autonomous Systems)

More information

Latest update

3/9/2022 8