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The ability to autonomously acquire new knowledge through interaction with the environment is one of the major research goals in the field of robotics. The knowledge can be acquired only if suitable perception-action capabilities are present. In other words, a robotic system has to be able to detect, attend to and manipulate objects in the environment. In the first part of the talk, we present the results of our long term work in the area of vision based sensing and control. The work on finding, attending, recognizing and manipulating objects in domestic environments is discussed. More precisely, we present a stereo based active vision system framework where aspects of foveated attention are put into focus and demonstrate how the system can be utilized for object grasping. The second part of the talk presents work on the visual analysis of human manipulation actions which are of interest for e.g. human-robot interaction applications where a robot learns how to perform a task by watching a human. A method for classifying manipulation actions in the context of the objects manipulated, and classifying objects in the context of the actions used to manipulate them is presented. The action-object correlation over time is then modeled using conditional random fields. Experimental comparison shows improvement in classification rate when the action-object correlation is taken into account, compared to separate classification of manipulation actions and manipulated objects.