Name
Abstract | ||
|---|---|---|
Grasping and manipulating everyday objects in a goal-directed manner is an important ability of a service robot. The robot needs to reason about task requirements and ground these in the sensorimotor information. Grasping and interaction with objects are challenging in real-world scenarios, where sensorimotor uncertainty is prevalent. This paper presents a probabilistic framework for the representation and modeling of robot-grasping tasks. The framework consists of Gaussian mixture models for generic data discretization, and discrete Bayesian networks for encoding the probabilistic relations among various task-relevant variables, including object and action features as well as task constraints. We evaluate the framework using a grasp database generated in a simulated environment including a human and two robot hand models. The generative modeling approach allows the prediction of grasping tasks given uncertain sensory data, as well as object and grasp selection in a task-oriented manner. Furthermore, the graphical model framework provides insights into dependencies between variables and features relevant for object grasping. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/TRO.2015.2409912 | Robotics, IEEE Transactions |
Keywords | Field | DocType |
Robot sensing systems,Grasping,Probabilistic logic,Data models,Planning,Training | GRASP,Computer science,Bayesian network,Artificial intelligence,Probabilistic logic,Graphical model,Robot,Mixture model,Machine learning,Encoding (memory),Service robot | Journal |
Volume | Issue | ISSN |
PP | 99 | 1552-3098 |
Citations | PageRank | References |
11 | 0.56 | 36 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dan Song | 1 | 31 | 1.77 |
| carl henrik ek | 2 | 327 | 30.76 |
| Huebner, K. | 3 | 51 | 2.37 |
| Danica Kragic | 4 | 2070 | 142.17 |