MIT researchers have developed a deep learning neural network
to aid the design of soft-bodied robots.
There are some tasks that traditional robots — the rigid and metallic kind — simply aren’t cut out
for. Soft-bodied robots, on the other hand, may be able to interact with people more safely
or slip into tight spaces with ease. But for robots to reliably complete their
programmed duties, they need to know the whereabouts of all their body parts.
That’s a tall task for a soft robot that can deform in a virtually infinite number of ways.
MIT researchers have developed an algorithm to help engineers design soft robots that collect
more useful information about their surroundings.
The deep-learning algorithm suggests an optimized placement of sensors within the robot’s body,
allowing it to better interact with its environment and complete assigned tasks. The
advance is a step toward the automation of robot design. The system not only learns a given task,
but also how to best design the robot to solve that task. The researcher says that sensor
placement is a very difficult problem to solve. So, having this solution is extremely exciting.
The research will be presented during April’s IEEE International Conference on Soft Robotics
and will be published in the journal IEEE Robotics and Automation Letters.
Creating soft robots that complete real-world tasks has been a long-running challenge in
robotics. Their rigid counterparts have a built-in advantage: a limited range of
motion. Rigid robots’ finite array of joints and limbs usually makes for manageable calculations
by the algorithms that control mapping and motion planning. Soft robots are not so tractable.
Soft-bodied robots are flexible and pliant — they generally feel more like a bouncy ball
than a bowling ball. The main problem with soft robots is that they are infinitely dimensional.
Any point on a soft-bodied robot can, in theory, deform in any way possible. That makes it tough to
design a soft robot that can map the location of its body parts. Past efforts have used an external
camera to chart the robot’s position and feed that information back into the robot’s control program.
But the researchers wanted to create a soft robot untethered from external aid.
The researchers developed a novel neural network architecture that both optimizes
sensor placement and learns to efficiently complete tasks. First, the researchers divided
the robot’s body into regions called “particles.” Each particle’s rate of strain was provided as an
input to the neural network. Through a process of trial and error, the network “learns” the
most efficient sequence of movements to complete tasks, like gripping objects of different sizes.
At the same time, the network keeps track of which particles are used most often,
and it culls the lesser-used particles from the set of inputs for the networks’ subsequent trials.
By optimizing the most important particles, the network also suggests
where sensors should be placed on the robot to ensure efficient performance.
For example, in a simulated robot with a grasping hand, the algorithm might suggest that sensors be
concentrated in and around the fingers, where precisely controlled interactions with the
environment are vital to the robot’s ability to manipulate objects. While that may seem obvious,
it turns out the algorithm vastly outperformed humans’ intuition on where to site the sensors.
The researchers pitted their algorithm against a series of expert predictions. For three different
soft robot layouts, the team asked roboticists to manually select where sensors should be placed
to enable the efficient completion of tasks like grasping various objects. Then they ran
simulations comparing the human-sensorized robots to the algorithm-sensorized robots. And the
results weren’t close. The researchers says their model vastly outperformed humans for each task.
This work could help to automate the process of robot design.
Automating the design of sensorized soft robots is an important step toward rapidly
creating intelligent tools that help people with physical task. The sensors
are an important aspect of the process, as they enable the soft robot to “see”
and understand the world and its relationship with the world.