A new system can reorient over 2,000 different objects, with the robotic hand facing both
upwards and downwards.
At just one year old, a baby is more dexterous than a robot.
Sure, machines can do more than just pick up and put down objects, but we’re not quite
there as far as replicating a natural pull toward exploratory or sophisticated dexterous
manipulation goes.
Artificial intelligence firm OpenAI gave it a try with Dactyl , using their humanoid robot
hand to solve a Rubik’s cube with software that’s a step toward more general AI, and
a step away from the common single-task mentality.
DeepMind created “RGB-Stacking,” a vision-based system that challenges a robot to learn how
to grab items and stack them.
Scientists from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL),
in the ever-present quest to get machines to replicate human abilities, created a framework
that’s more scaled up: a system that can reorient over 2,000 different objects, with
the robotic hand facing both upwards and downwards.
This ability to manipulate anything from a cup to a tuna can to a Cheez-It box could
help the hand quickly pick-and-place objects in specific ways and locations — and even
generalize to unseen objects.
This deft “handiwork” — which is usually limited to single tasks and upright positions
— could be an asset in speeding up logistics and manufacturing, helping with common demands
such as packing objects into slots for kitting, or dexterously manipulating a wider range
of tools.
The team used a simulated, anthropomorphic hand with 24 degrees of freedom, and showed
evidence that the system could be transferred to a real robotic system in the future.
This type of “in-hand” object reorientation has been a challenging problem in robotics,
due to the large number of motors to be controlled and the frequent change in contact state between
the fingers and the objects.
And with over 2,000 objects, the model had a lot to learn.
The problem becomes even more tricky when the hand is facing downwards.
Not only does the robot need to manipulate the object, but also circumvent gravity so
it doesn’t fall down.
The team found that a simple approach could solve complex problems.
They used a model-free reinforcement learning algorithm with deep learning, and something
called a “teacher-student” training method.
For this to work, the “teacher” network is trained on information about the object
and robot that’s easily available in simulation, but not in the real world, such as the location
of fingertips or object velocity.
To ensure that the robots can work outside of the simulation, the knowledge of the “teacher”
is distilled into observations that can be acquired in the real world, such as depth
images captured by cameras, object pose, and the robot’s joint positions.
They also used a “gravity curriculum,” where the robot first learns the skill in
a zero-gravity environment, and then slowly adapts the controller to the normal gravity
condition, which, when taking things at this pace, really improved the overall performance.
While seemingly counterintuitive, a single controller (known as brain of the robot) could
reorient a large number of objects it had never seen before, and with no knowledge of
shape.
Many small, circular shaped objects (apples, tennis balls, marbles), had close to 100 percent
success rates when reoriented with the hand facing up and down, with the lowest success
rates, unsurprisingly, for more complex objects, like a spoon, a screwdriver, or scissors,
being closer to 30 percent.
Beyond bringing the system out into the wild, since success rates varied with object shape,
in the future, the team notes that training the model based on object shapes could improve
performance.