Jenga is a complex game
requiring a steady hand and nerves of steel.
As humans, we combine our senses of sights
and tough to master this game.
Now, researchers at MIT's MCube Lab
have devised an algorithm to replicate this
ability using a robot.
Unlike typical machine-learning methods
that rely on huge data sets to decide their next best action,
this robot learns and uses a hierarchical model
that enables gentle and accurate extraction of pieces.
This model allows the robot to estimate the state of a piece,
simulates possible moves, and decide on a favorable one.
It divides the possible interactions
between the robot and the Jenga tower
into clusters, each with its own set of physics.
The robot efficiently and clearly identifies
when a piece feels stuck or free and decides
how to extract it using far less data.
This approach as a successful example of AI
moving into the physical world.
The robot learns as it interacts with its environment
and captures some of the essential skills that
define human manipulation.