I recently caught news of OpenAI leading a $23 million investment in
a humanoid robotics company based out in Norway - 1X Technologies.
The company is unrelated to the cable company with the same name based out in Sheridan,
Wyoming. This one used to be called Halodi.
The investment reminds me that one of OpenAI's early product goals was
to create a household robot capable of setting and
clearing an table. I reckon it's been a bit forgotten with all the ChatGPT stuff.
In recent days, there has been some growing investment interest in humanoid robots. The
mechanical and computational challenges are immense, but the dream is glorious.
In this video we are going to take a brief look at
the science and challenges facing the humanoid robot.
## Defining the Humanoid Robot
What makes a robot humanoid? There is no widely accepted definition.
Nevertheless, we can point to a number of generic characteristics stemming
from their design purpose. And from there, we can identify common traits.
These robots need to move around, perform a broad spectrum of tasks,
physically touch people without hurting them, and manipulate tools made for humans.
They also have to autonomously operate in human
environments as well as communicate with people at an advanced level.
When I first read that last bit, I wondered why that was necessary. Then I remembered how
many times I have been yelled at for messing up the chores back at home.
These design requirements thus dictate a human-like robot design with a torso,
head, two legs, and two arms. The arms should also have hands with
multiple fingers. Two legs are preferred over four for energy and cost reasons.
The technological bases of humanoid robots are in industrial robots, but there are significant
differences. Most industrial robots are nailed to the ground and built to perform a specific task.
They are big, heavy, and fast so most are not meant to regularly interact with ordinary people.
That is not the case with the humanoid robot. This thing is with you in tight,
intimate spaces. ￼And that means some special engineering.
## Beginnings
People have been dreaming about humanoid automatons.
Even back during the days of Leonardo Da Vinci, long before the word "robot" was ever invented.
So let us start our brief history of the humanoid robot starting in the 20th century.
It begins of course in Japan. A grieving father named Doctor Tenma embarked on a
solo project to create a replacement for his son, who had died in a car accident.
The robot, named Astroboy, then fights crime and injustice and ... oh wait, excuse me.
I am being told that Astroboy was really a manga created by Osamu Tezuka in the 1950s. Apologies.
## Zero Moment Point
One of the big moments in humanoid robotics
development was the introduction of the Zero-Moment Point stability concept.
Introduced in 1968 by the scientist Miomir Vukobratović,
the theory covers the dynamics and control of legged robots with powered, controllable joints.
An industrial robot is often bolted to the ground using a
single fixed anchor point. But the sole of a humanoid robot's foot is not fixed.
It only makes periodical contact with the ground. When you are walking,
you only have both feet on the ground about 20% of the time.
The vast majority of the time, the body is supporting itself on a single
foot. Did you walk around now to see for yourself? I did. Anyway, so the robot has
to determine whether or not a specific foot movement will allow it to keep its balance.
The Zero Moment Point represents the point on the ground where the robot's
footing can be stable. Calculating where it is and making sure that it
is located at the robot's foot is a vital part of the system.
Researchers develop control algorithms for achieving this. The most common ones approximate
the walking motion as like an inverted pendulum, but there are others around too.
## Wabot
A team at Waseda University led by Dr. Ichiro Kato started studying
humanoid robots based on previous work done on active prothesis development
In 1973, they debuted a robot known as Wabot-1. It walked on two legs,
touched and held things, and can communicate in Japanese.
The team judged it to have the mental faculties of a one and a half year old.
In 1984, they produced the robot WL-10RD, the first dynamically balanced bipedal robot.
It was the first of its type capable of walking and a physical demonstration
of the Zero Motion Point concept's feasibility for robotic bipedal walking.
The Waseda team then came up with a second robot Wabot-2 that can sight-read music and
play the electric organ - like any sensible Asian child. The robot's
movements were guided by 80 microprocessors and mechanically produced using wires.
## Honda and the P2
Then in 1986, the automobile giant Honda started
a humanoid robot research program to explore new ways of locomotion.
Their intention being to eventually produce a sophisticated and useful
robot capable of helping someone as like a work partner or servant.
They began with a bipedal prototype that used linear actuators - which
are like artificial muscles - to put one foot in front of the other.
This first robot - called E0, E for electronics - walked very slowly - 5 to 30 seconds between steps
- and only in a straight line. Honda expanded on this initial success by speeding up the pace.
Ten years later, they introduced the P2, a fully self-contained
robot capable of walking around on flat surfaces and even up stairs.
Prior to P2, many people were skeptical that it would even be possible to create
a working walking bipedal humanoid robot. So this achievement was a real breakthrough.
Honda's work has since evolved into the Humanoid Robotics Project, a government
sponsored project with the goal of eventually producing general domestic helper robots.
Their robots have been able to perform many individual movements like getting
up or squeezing through tight spaces in walls. It is probably one of the leading
developers in the international humanoid robotics industry.
## Balance & Movement
Humanoid robots can move on two legs. But many are not ready yet to move outside of the laboratory.
This is due to the Zero Moment Point-based
nature of these machines - which has several recognizable characteristics.
One is that when the robot is walking and one of its feet is in the air, the other foot has
to always be in full contact with the ground. It gives this sort of heavy flat-footed walk-stomp.
We do a lot of studies of how real humans walk as inspiration. But
the human body is very flexible. It can offer up to 300 different
degrees of freedom. While walking, the person uses up to 20 of them.
Scientists thus often simplify - freezing and rigidly controlling
certain joints - which again makes the walk or movement appear unnatural.
So most of these robots end up doing something like a bent-knee,
frozen-torso walking. It keeps your core more stable and is easier to calculate but is also
tiring. You try it. It is tiring. It is as tiring for the robot as it is for the human.
Part of the reason why it is so tiring is the lack of something called "toe-off".
This is where your trailing big toe pushes off the ground as your weight transfers
forward. It is an important energy-saving feature that stores energy in the leg.
Anyway, one other thing about bent-knee walking is
that it also makes it harder for the robot to clear obstacles. Previously,
walking robots could not deal with unexpected, unplanned obstacles over 6% of its leg length.
That is shorter than the average curb, and is partly because you
have so little clearance. There are also issues involving the problems of
calculating new coordinates when dealing with unexpected obstacles on the fly.
Though there are encouraging results with using AI to get
approximate results without having to do all the model calculation.
Progress in this particular area has been very encouraging. In 2018, a French team
demonstrated a robot capable of climbing normal, different-sized steps without the use of tethers.
That same year, an American team taught a Boston
Dynamics robot platform called Atlas how to walk in a straight-leg manner.
Interestingly enough, this straight-leg walking - as well as toe-off behavior - emerged naturally
after biasing the robotic body's controller to straighten the legs as much as possible.
There are also some really interesting tests that can
produce faster bipedal running speeds by studying birds rather than humans.
One prominent example is the Cassie robot by Agility Robotics, which can run the 100 meters
in under 25 seconds. Bird-running robots are very fast and can better navigate unstable terrain.
The only real thing is that they act and run like birds - which are
literally dinosaurs. This can affect how people might interact with them.
## Manipulation
People are not only great at walking around and keeping their balance.
They are also quite good at manipulating items.
Manipulating items remains one of the big problems in robotics.
Consider again OpenAI's goal of having a robot that can set and clear a table. That would mean
being able to pick up a dirty plate off the table, and then setting that plate into a tub.
How hard can it be? Feels quite easy and instinctive. But then again imagine all the
little subtle finger and hand movements that go into just picking up a plate.
Your hand goes to the plate, choosing an optimal trajectory to approach it.
The fingers gently slide along the plate's edge and then roll under it.
You push the hand and fingers forward enough to gain a grip over the bottom
of the plate in order to manipulate it, tip it up, and then lift it.
This difficulty is an example of Moravec's Paradox - the observation that certain things that humans
find instinctively easy - like clearing a table - are discovered to be very difficult for robots.
## Soft Touch
This action - and others like it - not only requires exceptional
mental calibration but also a soft touch.
The latter is something that has been surprisingly challenging for robotic muscles to do. Being
gentle requires precise feedback, acute physical awareness, and controlled stiffness. Machines are
good at being strong, rigid and powerful. Not so good at being soft and huggable.
The technology known as soft actuation remains a developing field,
requiring expertise in disparate fields like materials, structural design, and fabrication.
It is also computationally intense. Because the robotic hand is covered in a soft material that
moves and bends - often but not always silicone - the system's dimensions are basically infinite.
Calculating all the possibilities is unfeasible - even not counting the uncountable trajectories
a hand can achieve a certain movement in a certain way. So researchers essentially assume
rigid rather than soft body conditions. Such an assumption can lead to errors.
Sensors are also a big limiting factor in soft actuation research. Scientists need a
lot of sensing and feedback, so the sensors have to be cheap and easily manufactured.
Experiments have used sensors made from embedded liquid metal,
embedded rare earth magnets, optical fiber, and more. Trying to get the right
balance of price and sensitivity is challenging, to say the least.
Other major challenges for soft actuation involves power, manufacturability, and
noise. The most commonly used soft actuators use air - pneumatics. Such soft pneumatic
actuators often require power-hungry, loud, and high-vibration support systems like pumps.
## Doing It All
An industrial robotic arm can pick up a plate and put it into a tub.
Pretty well, actually. Gentleness notwithstanding. But that is not enough. A good humanoid robot
will also have to carry the tub with the plates in it over to the sink.
And then it goes back to the table with fresh plates,
setting them down properly and in a coherent order. We need the robot to do
all of these things well. And in varying locations under uncertain conditions.
Recently, Facebook released some interesting AI research on these concerns. They call it
Adaptive Skill Coordination, and they describe it as like chaining together
"lower-level" individual skills like picking something up. It can use those skills to
navigate towards achieving a bigger, longer-term goal in the real world.
I am for some reason reminded of Ultra combos in Killer Instinct. Loved that game when I was a kid.
Very importantly, Adaptive Skill Coordination allows the robot
to actually back up and correct itself when something goes wrong.
They used it to train a four-legged Boston Dynamics robot to perform a pick and place
task between two apartments, overcoming various uncertainties and challenging situations.
## Simulating
One of the ways researchers are generating the data to develop
proficiency in these activities are in simulation.
In a previous video, I talked a bit about the use of video
game technologies to produce simulated worlds for self-driving training data.
There are similar applications for humanoid robotics,
and their introduction has been a huge boon for the field. We can create simulated worlds
to challenge robotic control software without the need for the actual robot.
There is an interesting simulated competition called RoboCup 3D Soccer Simulation. It is
a research soccer game environment that uses the Nao humanoid robot as the agent.
The goal has been to help generate data for teams to create the low-level
behaviors like walking, standing up, and turning. For instance,
creating the kicking behavior - resulting in some pretty nice kicks.
## Communicating
Large Language Models are revolutionizing everything.
Can they do something for the robotics field?
The application is not entirely obvious. LLMs are trained on texts
and do not have bodies to sense their surroundings nor perform physical acts.
Not being aware of the physical context can lead to some strange answers. For example,
if I was asked to clean up some spilled coke,
I shouldn't suggest a vacuum cleaner if there isn't one nearby.
So we need to give the model that context in some way. The easiest would be to do
some intense prompt engineering to give the LLM all the context,
but that is not how humans are used to communicating.
I read an interesting paper from Google in which they trained a language model
in concert with low level skills - the "body" to the "brain". That is pretty cool.
But the most useful way to use a ChatGPT for robotics purpose would
be what Microsoft suggested. They use the LLM to translate ordinary
communications to something a machine can understand, like code or an API output.
But considering how ChatGPT and other LLMs like it are still apt
to providing incorrect answers, a person must remain in the loop.
Such a "brain in a bottle" thing can feel a bit
awkward and ChatGPT would be the first to tell you that.
## Conclusion
In some ways, producing the autonomous humanoid robot is more challenging than self-driving.
There are way more degrees of freedom for the humanoid robot to handle,
the human interaction is way more intimate and at
least with driving we know what the machine’s physical form will be like.
The economics are somewhat complicated too. But it’s hard to say exactly how
until we get a price. Price and manufacturability are critical
points that I just barely touched on in this video. But make no mistake, it matters a lot.
But there is a lot of investment interest nevertheless. You have
funding events for humanoid robotics companies like Apptronik, Agility,
1X, and Sanctuary. Some of these machines will start entering warehouses. The dream is real.
The deep technology is fascinating, and I do believe that new AI methods
will accelerate progress in humanoid robotics very quickly.
I don't know where it is going, but I am excited to see what happens.