You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: The grasp prediction CNN was trained using a dataset of over 800,000 grasp attempts, collected using a cluster of similar (but not identical) robotic manipulators, shown in Figure 1, over the course of several months.Although the hardware parameters of each robot were initially identical, each unit experienced different wear and tear over the course of data collection, interacted with different objects, and used a slightly different camera pose relative to the robot base. These differences provided a diverse dataset for learning continuous hand-eye coordination for grasping. Another related area to our method is visual servoing, which addresses moving a camera or end-effector to a desired pose using visual feedback (Kragic & Christensen, 2002). In contrast to our approach, visual servoing methods are typically concerned with reaching a pose relative to objects in the scene, and often (though not always) rely on manually designed or specified features for feedback control (Espiau et al., 1992; Wilson et al., 1996; Vahrenkamp et al., 2008; Hebert et al., 2012; Mohta et al., 2014). Photometric visual servoing uses a target image rather than features (Caron et al., 2013), and several visual servoing methods have been proposed that do not directly require prior calibration between the robot and camera (Yoshimi & Allen, 1994; Jägersand et al., 1997; Kragic & Christensen, 2002). To the best of our knowledge, no prior learning-based method has been proposed that uses visual servoing to directly move into a pose that maximizes the probability of success on a given task (such as grasping).

Question: How were the variations of the camera poses for the different robots determined?
A slightly different camera pose was selected for each robot, relative to the robot base. Another related area to our method is visual servoing, which addresses moving a camera or end-effector to a desired pose using visual feedback.