Stairs.
For thousands of years, the bane of robots
everywhere.
In this video, we’re going to explore the
challenges of footstep planning with a dynamic
robot like Cassie.
We also have some recent results that we’re
excited to share with you, and that will be
included in an upcoming product.
Lots of people have noticed that Cassie transitions
from standing to marching in place, then starts
walking.
This behavior was inherited from Cassie’s
predecessor robot ATRIAS, and is a consequence
of optimizing for steady-state behavior.
Cassie, like ATRIAS, is always actively balancing.
And Cassie, like ATRIAS, lacks any outward
looking sensors and is effectively blind to
the world.
Many examples of complicated terrain can be
easily navigated if you ignore the actual
state of the world and treat all obstacles
as gait disturbances.
For example, ATRIAS could walk over obstacles
by simply assuming that the world was flat.
If this was a bad assumption, no big deal.
The underlying gait dynamics were adequate
to absorb the uncertainty.
We’ve previously shown that Cassie can perform
similarly.
In this demonstration, Cassie is able to walk
forwards, backwards, and sideways over an
unknown obstacle without having any knowledge
of the environment.
Cassie thinks the world is flat, and the combination
of passive dynamics and software control is
robust enough for this to not matter.
This sort of behavior is what allows Cassie
to operate blind, outdoors.
Cassie doesn’t need perfect knowledge of
the ground to transition between different
surfaces or walk over roots.
In fact, this clip was part of nearly an hour
of continuous walking on an unpaved trail
without falls.
Unfortunately, at some point the complexity
of the world exceeds what the gait can accommodate,
and advance planning is required.
We’ve been hard at work on this problem
for a while, and we’re pleased to share
some recent results.
We first extend our dynamic gaits with planned
footstep placements along the direction of
travel.
This allows a continuous transition from standing,
to multiple steps, and back to standing, while
utilizing the dynamic stability shown in the
first half of this video.
Notice that transitioning from standing to
marching in place is no longer required.
Next, the planner is extended to include the
vertical direction, allowing individual foot
placement in 3 dimensions.
When the controller uses both legs and adds
a smooth transition, Cassie takes a full step
up.
This behavior is dynamic - Cassie is unstable
in the static sense at all times.
This step height, 19 centimeters, is approximately
what building code allows for stairs.
And for the curious: yes, Cassie is attempting
to maintain a constant pelvis height above
the floor during this test - you wouldn’t
ordinarily do this in most circumstances.
Finally, we extend this to an actual staircase.
First, with a single step
And then, all the way to the top.
This control framework combines the best traits
of kinematic planning approaches and pure
dynamics.
Because no robots were harmed in these videos,
here’s two more clips from our test process.
In this test, we told Cassie to take two steps
up.
There is actually only one step, but, since
Cassie is still actively balancing, she doesn’t
fall over.
If you’ve ever gone up or down a staircase
at night while not paying attention, you’re
probably familiar with how this feels.
This is a great demonstration of how planned
foot placement and active balancing are frequently
at odds with each other, when knowledge of
the world is imperfect.
Finally, we show that perception is still
required in order to time the initial foot
placement to the staircase.
As our engineers say, this is when a spring
loaded inverted pendulum becomes a regular
pendulum.
Sorry, Cassie.
