Putting a small network connector into its socket is harder than it looks. The plug must face the right way, line up and enter without slipping. A team led by Ammar Abbas compared ways to teach a five-fingered robot hand this task. They carried out roughly 1,700 attempts on real equipment, not just on a screen.

The team tested 281 versions of control software from seven groups. They changed what information the program received from cameras and how it described finger movements. This is called a data representation^[1]: the same task can be described using different pictures and numbers. Sometimes that choice mattered more than ordinary adjustments to the training process.

The researchers also examined 67 scores that could be calculated without performing the insertion. Only five, related to how smooth and direct the predicted motion was, closely matched real success. Even those worked only within one group of programs. A movement that looks excellent in a computer test may still miss the actual socket.

The findings concern particular hands, connectors and test conditions. They do not establish the best program for every object. They do explain why a robot must be tested with the things it will really handle. The connector still needs to be tested with a real hand, not just with software scores.