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Controlling Devices by Using Robot Arm
By Jinsung Kwon and Jaekwang Lee

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1. Introduction
We are willing to explore the possibilities to control several devices, such as mouse, keyboard,
etc., which are around us, using robot arm. Among those devices, we apply machine learning
theory in controlling the mouse. Mouse is a good example of devices which are hard to use
control theories, because the relation ship between input (external force) and output (mouse pointer
movement) is non-linear and difficult to define. For example, if we slide the mouse on the same
side with the same force several times, the reactions of the cursor would be different according to
the contact points.
To experiment this subject, we used PUMA560 robot arm in the Robot Manipulation Lab. We
can read the mouse cursor movement and get a lot of movement data for machine learning by letting
the robot arm touch the mouse in several possible ways and directions. We applied two learning
methods, which are linear regression and reinforcement algorithm to move the mouse to desired
position.
At section 2, we are going to introduce briefly about PUMA560 robot arm and our control
interface. At section 3, we will compare linear regression and reinforcement algorithm in moving
mouse cursor straightly. At section 4, we will use reinforcement algorithm in changing mouse
cursor direction.
2. Controlling Robot Arm
3. Learning to move straightly (Linear Regression vs. Reinforcement)
3.1. Linear regression
3.2. Reinforcement
3.3. Testing the algorithms
3.4. Conclusion
4. Learning to Change Direction
5. Conclusion
If we combine 2 and 3, we can move the mouse cursor to desired position. By this, we can
apply machine learning theories in controlling mouse without any complicated control theory or
mouse position sensors. Moreover, if we use different shaped mouse, it’s very difficult to find
proper control rules, but we can use machine learning by applying this method to get the
satisfactory result with any shape of mouse.

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