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one moves the arm or how continuous its motion is. If the subject stops to think how to proceed, this in itself will not increase the path length, but it will increase the time to task completion. The harder the task, the more thinking the operator needs, and the more time he or she takes to think. The relation of task hardness to the length of path is hence more subtle than its relation to the time to completion. This may be a useful consideration for balancing advantages and disadvantages of virtual versus physical control means in real-world teleoperation systems.
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Effects of the Visibility Factor. This factor refers to the obstacles in the environment being visible or invisible to the subjects during the test. The test subjects themselves, researchers, and practicing operators usually think that seeing the robot surroundings would signi cantly improve their performance. Interestingly, our study suggests that while this common sense judgment applies to very easy tasks, it does not apply to relatively complex tasks. For the easier task in this study (which is moving the arm left-to-right), there is only a slight difference in the resulting path length and completion time. That is, seeing the environment helped a little in path length and in completion time. On the other hand, for the more dif cult task (moving the arm right-to-left,) there was almost no difference in the path length and completion time. This looks puzzling, but becomes less so if one considers that many studies have demonstrated that humans are, in general, not very good in spatial reasoning based on visual data. This fact questions the large resources that are often allocated in telerobotics to help the operator see the scene. It also implies that the operator performance is affected less by the visibility factor than by the human spatial reasoning abilities. Effects of the Training Factor. This factor has two components that refer to the day of the task execution: day 1, before training, and day 2, after training. When comparing human performance on those two days, with the other conditions xed, any statistically signi cant difference should be attributed to the effect of training. Namely, a signi cant difference would support a common wisdom hypothesis that one s performance should improve signi cantly after learning from repeated exercise. This study shows that in arm manipulator motion planning tasks, training has no signi cant effect on human performance, neither in terms of path length nor in the task completion time. In our tasks the subjects were unable to seriously improve their motion planning skills via training. This is no doubt very surprising. One would expect the opposite conclusion: We all know examples of tasks involving motion where, given enough training, humans become extremely adept; an acrobat on the trapeze is but one example. There is a big difference, however: The acrobat does a once-and-for-all learned motion, whereas our tasks require constant spatial reasoning. Our test protocols do not allow a subject to simply memorize a task. We want our subjects to learn how to do a class of tasks; we want them to improve their spatial reasoning skills, rather than memorize a speci c motion. Examples of positive effect of training in tasks that involve spatial reasoning are harder to come up with. Note that since
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HUMAN PERFORMANCE IN MOTION PLANNING
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the tasks given to subjects in this study are quite close to teleoperation tasks, the reported results should be taken seriously by designers of teleoperated systems. There was one exception from this pattern: The training factor had a slight positive effect on the subjects overall performance in Experiment Two (see the box Experiment Two and combined data, Table 7.21; the extent of improvement is only 3.67%). The meaning of this exception is not clear. Given that the effect disappears for the combined Experiments One + Two data (further in the same box in Table 7.21), the small effect of the training factor for the Experiment Two data might be an artifact due to the insuf cient data or measurement errors. Or, training may indeed improve though only a little bit human performance in motion planning tasks such as ours.
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Effects of the Motion Direction Factor. This factor has two components, left-toright direction and right-to-left direction of motion. These two tasks took place in the same scene and with the same two-link arm manipulator. The only difference was that in the rst task one was asked to move the arm from position S (start) to position T (target) (Figure 7.5, Section 7.2.2), and in the second task one would go from T to S. In this study the motion direction factor happened to have a signi cant effect on the subjects performance. In fact, the effect has been stronger than other effects observed. Hence the motion direction factor was included in the study, to help assess the effect of the task dif culty on one s performance, with or without other factors involved. Using the same scene and the same arm in both tasks has an added advantage that the perceived dif culty of one task over the other is then known to be in one s head only. After all, a human subject could in principle produce exactly the same path in both tasks, which is what a robot algorithm would do.12 The unequal dif culty of the two tasks as perceived by the subjects is very interesting. It suggests that human performance is limited by human motion planning skills no less than by the task s objective complexity. As a minimum, it demonstrates a profound qualitative difference between the human and robot algorithms. Why do human subjects perceive the above two tasks as completely different It is as if changing the direction of motion to right-to-left produces some additional, if unclear, dif culties; perhaps it adds more possibilities for motion planning or more ways to make mistakes. In Section 7.2.3 we made an attempt to speculate about the reasons affecting human performance in these two tasks (see design comment No. 4 and Figure 7.9).
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Formally, this is not exactly so. For example, in the example in Figure 3.5, Section 3.3.2, the path shown from point S to point T is produced using the local direction left. If the same algorithm (here Bug2) now starts from T toward S, using the same local direction, the resulting path will be different from the one shown: It will be complementary to the shown path in that it will pass around parts of obstacles that were not passed when moving from S to T . The same is true for the arm manipulator algorithms discussed in s 5 and 6. The nature of this difference is, however, not the same as in human performance. By simply switching the algorithm s local direction to its opposite, we will obtain a path identical to the one shown in Figure 3.5. Whatever rules guide human motion planning strategies, they must be very different.
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