MIT researchers have developed a system that improves the speed and agility of legged robots
as they jump across gaps in the terrain.
In recent years, four-legged robots inspired by the movement of cheetahs and other animals
have made great leaps forward, yet they still lag behind their mammalian counterparts when
it comes to traveling across a landscape with rapid elevation changes.
Now, MIT researchers have developed a system that improves the speed and agility of legged
robots as they jump across gaps in the terrain.
The novel control system is split into two parts — one that processes real-time input
from a video camera mounted on the front of the robot and another that translates that
information into instructions for how the robot should move its body.
The researchers tested their system on the MIT mini cheetah.
Unlike other methods for controlling a four-legged robot, this two-part system does not require
the terrain to be mapped in advance, so the robot can go anywhere.
In the future, this could enable robots to charge off into the woods on an emergency
response mission or climb a flight of stairs to deliver medication to an elderly shut-in.
The use of two separate controllers working together makes this system especially innovative.
A controller is an algorithm that will convert the robot’s state into a set of actions
for it to follow.
Many blind controllers — those that do not incorporate vision — are robust and effective
but only enable robots to walk over continuous terrain.
Vision is such a complex sensory input to process that these algorithms are unable to
handle it efficiently.
Systems that do incorporate vision usually rely on a “heightmap” of the terrain,
which must be either preconstructed or generated on the fly, a process that is typically slow
and prone to failure if the heightmap is incorrect.
To develop their system, the researchers took the best elements from these robust, blind
controllers and combined them with a separate module that handles vision in real-time.
The robot’s camera captures depth images of the upcoming terrain, which are fed to
a high-level controller along with information about the state of the robot’s body (joint
angles, body orientation, etc.).
The high-level controller is a neural network that “learns” from experience.
That neural network outputs a target trajectory, which the second controller uses to come up
with torques for each of the robot’s 12 joints.
This low-level controller is not a neural network and instead relies on a set of concise,
physical equations that describe the robot’s motion.
The researchers used the trial-and-error method known as reinforcement learning to train the
high-level controller.
They conducted simulations of the robot running across hundreds of different discontinuous
terrains and rewarded it for successful crossings.
Over time, the algorithm learned which actions maximized the reward.
Then they built a physical, gapped terrain with a set of wooden planks and put their
control scheme to the test using the mini cheetah.
Estimating the robot’s state proved to be a challenge in some cases.
Unlike in simulation, real-world sensors encounter noise that can accumulate and affect the outcome.
So, for some experiments that involved high-precision foot placement, the researchers used a motion
capture system to measure the robot’s true position.
Their system outperformed others that only use one controller, and the mini cheetah successfully
crossed 90 percent of the terrains.
While the researchers were able to demonstrate that their control scheme works in a laboratory,
they still have a long way to go before they can deploy the system in the real world.