Prototype 1- Heisman Robot
We evaluated the Heisman robot by continual trial and error. The main feature of this robot is the cup stacking claw. When we tried this claw we encountered a problem when the robot reached the two cups near the shelter. We could not continue our robot's intended path of simply stacking the cups (because the tribbles would prevent the cups from sticking together), so we discussed the issue and made a group decision to add a projection from our cup grabber. This projection consists of two small bars pinned together in two locations which protude only an inch. The robot turns sideways and because the protrusion is at the correct height, it will knock the tribbles off. A more complex problem arose in that it is going to be difficult to hard-code the entire robot, i.e. problems in alignment and determing the exact right starting locations and locations of cups. This is leading us to want to add Sonar to the robot, although the decision is not finalized. This problem has led us to learn that behavioral structure may lead to a more successful robot.
Prototype 2- U-Robot
We again had to repeatedly test the efficiency of this robot's grabber. It is key to have perfect accuracy in our tribble placement because the bulk of this robot's points are scored with tribble. The intentions of the grabber is to release the plants over one bin, and the crew members over another bin (both bins are attached to the i-Create), but we have been having difficulties dropping the tribbles perfectly in the correct bin. In many of our tests, the tribbles miss the bin or fall into the incorrect bin. These lessons affected our design by causing us to construct a stronger claw with more accuracy. After discussing the issue, we decided to use our 36 square inches of paper to make a funnel to greatly increase the chance of the tribbles falling in their intended bins. So far, this design has proven itself to be worth the while, because after a surplus of tests, the robot's grabber has increased to nearly 100 percent efficiency. 
We have learned several critical lessons from the above tests. One, for example, is that a small amount of work in the planning stages saves vasts amount of time later on. If potential problems are overlooked or ignored, they will eventually occur, and we then need to address the problem in a much more frantic time period. Nonetheless, an abundance of trials which test every aspect of our bot are essential to solidify our consistency and highlight our errors. In addition, we learned that we need a more accurate claw when placing the tribbles into the bin.