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The researchers tested their approach against other state-of-the-art machine learning algorithms, in a computer simulation of the game using the simulator MuJoCo. The lessons learned in the simulator informed the researchers of the way the robot would learn in the real world.
“We provide to these algorithms the same information our system gets, to see how they learn to play Jenga at a similar level,” Oller says. “Compared with our approach, these algorithms need to explore orders of magnitude more towers to learn the game.”
Curious as to how their machine-learning approach stacks up against actual human players, the team carried out a few informal trials with several volunteers.
“We saw how many blocks a human was able to extract before the tower fell, and the difference was not that much,” Oller says.
But there is still a way to go if the researchers want to competitively pit their robot against a human player. In addition to physical interactions, Jenga requires strategy, such as extracting just the right block that will make it difficult for an opponent to pull out the next block without toppling the tower.
For now, the team is less interested in developing a robotic Jenga champion, and more focused on applying the robot’s new skills to other application domains.
“There are many tasks that we do with our hands where the feeling of doing it ‘the right way’ comes in the language of forces and tactile cues,” Rodriguez says. “For tasks like these, a similar approach to ours could figure it out.”
This research was supported, in part, by the National Science Foundation through the National Robotics Initiative.
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