This paper proposes a probabilistic framework for sensor-based grasping and describes how information about object attributes, such as position and orientation, can be updated using on-line sensor information gained during grasping. This allows learning about the target object even with a failed grasp, leading to replanning with improved performance at each successive attempt. Two grasp planning approaches utilizing the framework are proposed. Firstly, an approach maximizing the expected posterior stability of a grasp is suggested. Secondly, the approach is extended to use an entropy-based explorative procedure, which allows gathering more information when the current belief about the grasp stability does not allow robust grasping. In the framework, both object and grasp attributes as well as the stability of the grasp and on-line sensor information are represented by probabilistic models. Experiments show that the probabilistic treatment of grasping allows improving the probability of success in a series of grasping attempts. Moreover, experimental results on a real platform using the basic stability maximizing approach not only validate the proposed probabilistic framework but also show that under large initial uncertainties, explorative actions help to achieve successful grasps faster. © 2013 Elsevier B.V. All rights reserved.
- Grasp planning
- Probabilistic models