Example of application of bayesian network learning and inference to robotics, and a brief but useful related work on learning by imitation

Dan Song; Ek, C.H.; Huebner, K.; Kragic, D., Task-Based Robot Grasp Planning Using Probabilistic Inference, Robotics, IEEE Transactions on , vol.31, no.3, pp.546,561, June 2015, DOI: 10.1109/TRO.2015.2409912.

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.

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