Task-Based Robot Grasp Planning Using Probabilistic Inference
2015 (English)In: IEEE Transactions on robotics, ISSN 1552-3098, E-ISSN 1941-0468, Vol. 31, no 3, 546-561 p.Article in journal (Refereed) Published
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.
Place, publisher, year, edition, pages
2015. Vol. 31, no 3, 546-561 p.
Electrical Engineering, Electronic Engineering, Information Engineering
IdentifiersURN: urn:nbn:se:kth:diva-170982DOI: 10.1109/TRO.2015.2409912ISI: 000356518700003ScopusID: 2-s2.0-84926395738OAI: oai:DiVA.org:kth-170982DiVA: diva2:841369
QC 201507132015-07-132015-07-132015-07-13Bibliographically approved