The goal of the Learning Locomotion program is to develop a new generation
of learning algorithms that enable traversal of large, irregular obstacles by
unmanned vehicles. By learning from experience, these algorithms will allow
robots to function in more variable and unexpected terrain than hard coding
DARPA has selected six university research teams, including ones at MIT and
Stanford, to compete to develop the best algorithms for controlling the robot
puppy. The agency hopes this will help identify the best adaptive strategy for
moving over irregular surfaces.
From DARPA's website:
"Large, irregular obstacles such as urban rubble, rock fields, and
fallen logs present minor challenges to dismounted forces, slowing but not
stopping them. These Slow-Go areas for dismounted forces are No-Go areas for
today?s small unmanned vehicles, limiting their effectiveness on the battlefield.
Enabling future unmanned vehicles to traverse large, irregular obstacles will
allow robots to better contribute to military operations.
Locomotion over extreme terrain requires deliberately planned, precisely
coordinated movements. Like a hiker traversing a boulder field, the unmanned
vehicle in extreme terrain will succeed not by flailing, but by meticulously
sequencing its motions. The complexity of the planning and the required sensorimotor
coordination presents significant challenges for the design and implementation
of control systems. Handcrafting the control laws and parameters may not even
be possible with reasonable effort.
Automatic learning offers a promising alternative. In the Learning Locomotion
program, algorithms will be created that learn how to locomote based on the
experience of a legged platform confronting extreme terrain. It is expected
that the performance of these algorithms will far exceed the performance of
handcrafted systems, creating a breakthrough in locomotion over extreme terrain.
Further, it is expected that these algorithms will be broadly applicable to
the class of ?agile? ground vehicles.
6 Performer teams will receive a locomotion platform called Little Dog,
with 4 legs, 3 actuators per leg, and a total weight less than 7 pounds. They
will also be given a board with built in terrain features acting as obstacles
of varying size and spacing.
On an approximately monthly basis, beginning approximately three months
into the period of performance, performer teams will upload their software
to a central facility. There, independent tests will be conducted by downloading
the code into a functionally identical Little Dog, and running it on a terrain
board that is statistically equivalent but not physically identical to the
teams? terrain boards. Performance will be measured by travel speed,
and by the size of the largest obstacle traversable.
Phase I is a 15-month effort to develop learning methods that control
autonomous travel over extreme terrain. The goal is for the system to travel
about 0.6 in/sec and scale obstacles 2.5 in tall. Phase II will be an 18-month
effort in which the desired speed will increase to 3.8 in/sec and the max
obstacle height will become 5.7 in. These speeds and heights were determined
by the leg length of the vehicle."
LittleDog is a timid-looking four-legged robot about the size of a Chihuahua.
Yet, it's small size makes it ideal towork with, as 'mountains' can be made
with ease for it to climb over, that could not be so easily done with a larger
The robot has three motored joints on each leg, and its movements are controlled
precisely by an on-board computer. An internal gyroscope lets the robot sense
its orientation, while an external motion-capture system monitors the precise
position of each limb and joint as it moves.
The video below, shows one of the LittleDogs in action, this one from Carnegie
Mellon University. It is hard to look at the first half of this video and not feel
sorry for the little critter. However, it, like any baby, has to learn to crawl,
before it can learn to walk. The robot in the second half of the video is the
same one; it taught itself.
This little bundle of metal and plastic, that tries so hard, is the first stage
in a DARPA project to create sophisticated robotic assistants for military personnel,
including automated "pack-mules" capable of hauling heavy loads over tough terrain.
Boston Dynamics has previously demonstrated a much larger four-legged robot
called BigDog. Internal sensors and motors allow this robot to rapidly regain
its balance after slipping or being pushed, but BigDog is unable to tackle the
kind of irregular terrain faced by LittleDog. It is also incredibly loud, requiring
hefty motors due to it's sheer weight. The hope is, however, to use thealgorithms
developed by LittleDog, to teach BigDog new tricks.