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Файл:Readings in Robotics Engineering. Учебное пособие
.pdf
1.8. Responding to a changing environment Video
31
Master the language: Connect the below ideas to make a sentence.
government / crush / uprising
e.g. The government plans to crush
the uprising.
affable / robot
escalate / war
slated / deploy / 2018
in the works / elderly
automaton / corrosion
withstand / temperatures
elaborate / software / glitch
rover / inhabitability
Master the language: Idiom: in the wake of
Meaning:
1) following something; coming after something
hint: a wake is a track that a boat or ship leaves in water.
A wake is a trail that always follows a boat. When something happens in the wake of something, it happens after it;
it follows it.
Example:
In the wake of the protests, the government agreed to lower
taxes.
Read and complete the below sentences:
In the wake of the Hiroshima bomb attack, Japan _________________ .
In the wake of __________________, I decided to ________________.
Design Project (Pair Work)
Your teacher is a rich investor. He/she has sponsored a robotics design
competition. The team who comes up with the best idea for a new robot will be
awarded 10 million dollars to have their product developed. You and your partner have entered the contest. You have 5 minutes to think of a new robotics
product. When finished, try to sell your idea to your teacher.
Debate (Pair Work)
*each student reads his or her role only
Student A:
You believe that space exploration is the key to the survival
of the human race. Take a minute to think of other reasons
why space exploration is important. When ready, ask your
partner what she/he thinks of the new mission to Mars.
Part I. Sensing the World
32
Student B:
Governments worldwide spend billions of dollars on
space exploration. You believe it’s a waste of time and
money. The Earth has many problems. We need to fix our
own problems instead of flying off into space. Think of
other reasons to support your argument. Your partner will
start the conversation.
Discussion Questions
1) Could a robot do your job?
2) Why are humans so interested in robots?
3) What is AI?
4) Will robots cause rising unemployment in the future?
Lesson plan copyright Matthew Barton of Englishcurrent.com
1.9. The challenges of making responsible decisions Video
So far, we have seen that in order for a robot to be autonomous it first
needs to be able to sense its environment and then to respond to it. But how
does a robot decide how to respond?
In this video, Owen explains why we need to design robots that are able
to make responsible decisions.
Discussion
What do you think responsibility means for a robot?
Transcript
0:05 If you’ve sent a robot out into the real world where there are people
around, potentially also people who don’t really know anything about the robot
- so a great example of that is a driverless car, driving out in a city, you’ve got
pedestrians and things like that - the people expect the robot to behave in a cer-
tain way, so they don’t expect to be run over by it. They expect it to behave
quite similar to how a person would behave. In other words, in a way that is
responsible. A human driving a car doesn’t just try and run people over, be-
cause they have responsibilities. And you expect the robot to behave in the
same sort of way.
0:37 But that’s actually quite challenging for a robot to do, because a
robot will do what it’s programmed to do. And so if you don’t program it in a
way that it is then expecting to make these responsible decisions, it won’t do
1.10. How can robots make responsible decisions?
33
that. It will drive into things because it doesn’t know they’re there, or it doesn’t
know how the environment’s going to change or anything like that. So it can be
very challenging in the real world where the environment is very dynamic.
1:01 A great example of responsible decisions in the real world, if you
like, that humans deal with every day is, if you’re driving down the motorway,
and the car in front of you brakes suddenly, you’ve got a couple of choices you
can make there. You can swerve, you can brake or you can do various things.
Now, if you swerve out into another lane, you might then end up in front of another car, which may cause a bigger accident than if you just brake and stay in
line. That is a very challenging set of thoughts that goes through the human brain.
1:29 And trying to do some of that stuff in robots is really quite chal-
lenging, because the process that you have to go through there is, you first of all
have to sense what’s going on in the environment and sense what that means in
the future. So, you’ve detected that the car in front is stopping. If you don’t do
anything, you’re going to hit it, so you have to take some action. But then, ra-
ther than just taking any old action, you have to think through, what happens if
I stop? Or what happens if I swerve? So what are the consequences of any action that I might take in the future?
1:59 And then, you kind of need to weigh those up to say which of these
is the best action for me to take, the most responsible action for me to take, to
not only minimise damage to myself, but minimise damage to other people in
the environment. The human brain’s very good at doing this. We’ve sort of
honed this skill of being responsible decision-makers. But robots are only just
really starting to learn how to do this, and it’s down to us to program them in
such a way that this kind of behaviour is inbuilt.
1.10. How can robots make responsible decisions?
Before acting, robots need to plan and make decisions, which is a key part
of cognition. But how do robots plan actions that will achieve their goals, whilst
guaranteeing the safety of those around them? In this step, we’ll look at some of
the ways that robots can be programmed to make responsible decisions.
The benefit of foresight
As humans, we have the ability to study a situation and make sure we
understand its dynamics before making a responsible decision. Understanding
Part I. Sensing the World
34
environmental dynamics means we have the foresight to predict the consequences of our actions and the actions of others, as well as events in nature.
If whilst driving, we notice a road block up ahead, we will start to slow
down so that we can come to a gentle stop. This is an example of foresight,
which is essential for making responsible decisions in complex environments.
Unfortunately for robots, foresight is not so simple. It can be built in part
by interpreting the environment. But the robot will need a sufficiently detailed
and complete model of the environment, with rapidly updated positions of the
other vehicles and pedestrians.
Predicting the consequences of planned actions
The consequences of a robot’s actions can vary. They may result in:
• a physical change of the environment
• a change in the robot’s location
• legal consequences (such as a drone not being allowed to fly over a
specific area)
• social consequences (such as causing a nuisance to neighbours)
There are two computational approaches for predicting the consequences of a robot’s actions:
1. We can physically simulate the robot’s actions in the environment to
predict the future physical state of the world.
2. We can use logic to infer legal and social consequences in terms of
breaking rules of behaviour.
Planning while keeping to rules
If concrete actions are not planned, the robot could use mathematical optimisation methods to decide how to act.
Mathematical optimisation is a technique for achieving the best possible result under given circumstances, called constraints.
This type of planning can go beyond computing the consequences: it can
optimise the consequences of the action to be taken for the best outcomes.
Game theory is a type of planning where each robot is considered to be
a player of a game and receives rewards dependent on the actions of the whole
robotic team.
1.10. How can robots make responsible decisions?
35
Using a well-defined set of primitive actions
Planning in continuous time for long periods can be computationally
heavy. Planning can be simplified if the robot performs only a limited set of
well-defined actions.
Primitive actions can reduce planning complexity by separating one
long, complicated task into a set of smaller and simpler discrete sections. These
sections are like building blocks that can be chained together to form complex
behaviour.
Example: Consider how you navigate through a building. “If I move
straight along this corridor, turn left and go through the first door on the right,
then I will be in the kitchen”.
The primitive actions in this example are:
1. “going straight”,
2. “turning left”,
3. “going through a door”.
These are much simpler to sequence together as a plan than breaking
down the problem into a large number of individual walking steps. Note that
the primitive actions have sufficient detail to define and accomplish the task but
are not overly-detailed.
The associated discrete abstractions of perceptions are “recognise the
end of the corridor”, “finding the first door on the right” and “being in the
kitchen”.
Symbolic planning
Symbolic planning uses a rapidly updated model of the environment to
decide on the most appropriate action of the robot.
Symbolic planning also needs to include rules of behaviour and take
into account the functional, safety and societal consequences of actions taken
by the robot.
For instance, robots searching a building need to take into account the
damage their search may cause, their own safety in case of a burning building, or the level of disturbance they may cause to related human activity.
All this can be based on symbolic computations about the timing of
events (temporal logic) and about knowledge and intent by others (epistem-
ic logic).
Part I. Sensing the World
36
1.11. Learning from good drivers Video
How will autonomous vehicles drive on the roads of today? How can we
ensure that they will drive safely alongside humans?
Automated technology in cars could help to prevent accidents and reduce congestion and emissions in cities. However, automated driving is highly
complex and requires systems to respond to a wide range of real-world driving
situations.
Professor Sandor Veres has been working with telematics company The
Floow LTD to develop automated vehicles that can learn from the behaviour of
human drivers.
In this video, we’ll first hear from Dr Sam Chapman, Chief Innovation
Officer at the Floow, as he explains the types of information that can be gathered from drivers and how this can be used to educate drivers, and improve
infrastructure and town planning.
We’ll then hear from Sandor about how this data is helping the Univer-
sity in their current research project, designing systems that use driver experienced based decision-making (DEBDAC).
Transcript
0:05 Autonomous driving is bound to come in the future. However,
when it comes, it may not be entirely safe. People talk about the prospects of
autonomous vehicles saving the 90% of crashes that are human-led. However,
human understanding leads to that machine understanding is not perfect either.
What we need to do is, we need to make sure that the understanding and fit of
these vehicles into the road architecture and their movements doesn’t cause a
shock to the system and introduce more chaos than it’s actually trying to save.
So what we need to do is we need to make sure that there is a firm understanding of risk.
0:42 Decisions are made based on the most available and the current information as best as possible, so that autonomous vehicles can fit in reliably
into the roads of today. I’ve been looking for a number of years now at human
driving behaviour. We gather many hundreds of thousands of vehicles in real
time, every time they’re moving in subsecond detail, of how they’re moving in
terms of the GPS, accelerometer, and a range of other sensors depending upon
the device fitted into the vehicle. This gives us a lot of information about how
1.11. Learning from good drivers Video
37
people behave, down to individual metre segments of every road in the world.
However, it does lead to an understanding of the individual vehicle, not actually the vehicle in context.
1:25 There is still a lack of certain information that we can’t tell if a ve-
hicle is having strange accelerating and braking behaviour because of vehicles
in front of them, or actually, it’s just the driver driving a little bit erratically,
because we don’t have the context of wider sensors. Autonomous vehicles
bridges that gap and allows us to examine more sensors and more context
around how people drive to get a better picture of what is safe, how to make
better decisions. And using this information in combination with traditional
systems for making decisions, such as vision-based systems - is there a car in
front of me? Therefore, slow down.
2:01 You can also start to take into account other information, like actually, do all other vehicles slow down at this point? Is this normalised behaviour,
or am I acting outside the norm of what human drivers would do? These can
lead to better decisions made inside autonomous vehicles. We gather this information on behalf of insurers with their opt-in customers who choose to opt
into policies that allow them to get significant savings on their insurance in
return for giving data about how they drive.
2:30 It eliminates things like whiplash fraud, and so on, thus allowing
insurance costs to be lowered, which is a saving for the insurer, a saving for the
driver, but also introduces technology that’s gathering essential data about the
condition of roads and the behaviour of people as they move. We’re looking for
behaviour that is predictive of risk ultimately, which in a human driving capacity is the drivers that drive more aggressively, the drivers that drive more irresponsibly. They drive outside the normal envelope of most other drivers. And
those drivers, when they have certain traits of behaviour, they can correlate to
claims from other drivers that have similar traits and performances. And from
that, you can use that to help educate.
3:10 So you can actually feed back and help to improve drivers. You can
actually inform them to become more responsible. You can actually improve
the general lot of an insurer’s book of policyholders. They can actually become
better. The information is also valuable in many other areas. It can help with a
whole range of things, from where to place the next IKEA, the next store, the
next thing to inform about road structures. Is the signalling on that lamp post
Part I. Sensing the World
38
timed right? Or is there certain other things that are operated and optimised as
best as possible about the road network?
3:43 What we’ve tried to do in this project that we’re starting now is,
we’ve tried to look at the data to see how we can use it to help improve autonomous driving. For some time, we have been studying autonomous driving at
the university, and we have come to the recognition that it’s a very hard prob-
lem as far as decision-making is concerned. The realisation was that this can’t
be done just by LIDAR and computer vision where the empty spaces are. There
is more complex decision-making at hand. And then we discovered The Floow
Ltd are gathering data as vehicles go on the road. We realised that there is an
opportunity here.
4:28 And the opportunity is to extend perhaps the data gathering, what
Floow Ltd is doing currently, and look at instantaneous data about a scene what is happening with the other participants of the traffic - and derive from
that the kind of decision-making that human drivers do. The DEBDAC is a
driver experience based learning system for autonomous cars. The title says
what it is. We are gathering data about drivers’ experiences. It drives hundreds
of miles on various roads, not only on motorways, on small roads, in urban
traffic, in dense traffic, in traffic jams, all kind of stuff.
5:12 And we were going to measure what other vehicles are doing, what
the pedestrians are doing, what is the weather, what traffic signs, what roadblocks in terms of road repair, what potholes appear, actually, on the road surface and so on. And we gathered this data into a huge database, and we analysed the decision-making of the human driver. And then we wanted this system to be transferred, basically to the autonomous car decision-making. The
purpose is to make decision-making in autonomous cars better. So at the moment, Google has their autonomous cars on the road, and also various universities have various autonomous cars on trial.
5:56 And we have reports about some kinds of consequential accidents
which are not necessarily because the autonomous cars make some mistake, but
it doesn’t understand the human context. Most often, the problem is that people
can’t stop suddenly when the autonomous car stops. And also, when the car
should be polite, it may not do exactly the right thing. It’s too abrupt. So it
doesn’t have the social situational awareness. We need to monitor that. Insur-
1.12. Humans making responsible decisions Video
39
ance companies are definitely very interested in that, that we have a very
smooth transition to autonomous car usage.
6:34 A car manufacturer should be very much interested in autonomous
driving capability decision-making, which in effect adapts itself to the human
condition, so driving when lots of other human drivers will be around still. And
this is a key factor to reduce the number of accidents. In this context, I strongly
believe that autonomous cars, in the future, will drastically reduce the number
of accidents on the road. And the public will quickly recognise their benefits in
terms of the accidents. That’s definitely the case. It takes some time for things
to mature, but in fact, the sensor technology of an autonomous car can be, in
the future, more advanced than a human capable of sensing, and faster.
7:23 And that’s quite a promising prospect for the future, that we will
have nearly accident-free roads, if it’s possible.
1.12. Humans making responsible decisions Video
We’ve talked about the importance of designing a robot to make respon-
sible decisions, but what about making responsible decisions when designing
robots?
In this video, Professor Noel Sharkey talks about some important ethical
considerations for developing autonomous robots.
Discussion
How do you think we can ensure a safe and ethical future with robots?
Transcript
0:05 Of course, one of the hopes for the future is autonomous cars, auton-
omous vehicles. And there’s a lot of talk about them saving a lot of lives on the
road. I kind of tend to believe that, that in the long run, that will be the case.
Google is the leading manufacturer of autonomous cars. And the thing about the
Google car is that the new one drives at 25 miles an hour and has no steering
wheel at all. So blind people can get into it, whatever. It’s quite an amazing thing
if you think about it. But the state of California have now made them put a driving wheel on it, because they’re concerned about how it will operate without one.
0:44 The law is you have to sit with your hands above the steering
wheel, the car drives itself, and be ready to grab it if there’s an emergency.
Part I. Sensing the World
40
Now, it’s hard enough to concentrate for two or three hours in the motorway,
never mind sitting there when you’re not in control at all. People are going to
drift away. They’re going to start looking at their phone - people already look at
their phone when they’re driving - start looking at their phone, reading. Sud-
denly, an emergency comes up, they’re not going to be able to handle it.
Google is starting to realise this. This is what happens with autopilots in planes.
1:12 Suddenly, you ask the pilot to get involved, and the plane crashes
because they’re not ready for it. What happens when one of these cars get in an
accident? There are some really crazy things being said at the moment in the
United States. I mean, there are about ten car manufacturers with these, and
they’re kind of on the brink at the moment. But the idea of having an accident
is that the car will work on this kind of ethics called consequentialist ethics,
where you try and minimise the number of people damaged. So the car comes
into an accident, searches around.
1:42 There’s one guy standing over there on his bicycle, there’s a group of
children over at the other side, and there’s a group of old people in a bus. The car
then decides to hit the guy who’s standing alone. And I can’t bear this because
it’s the idea is that - what has happened now - the car has legally become a weap-
on, because it’s targeting a particular individual. And I don’t know what they’re
going to do about that at all, I have no idea. You could make it random, but that’s
not a good solution either. But then there’s a lot of worries about accountability.
Google are the only people in California who insure their car.
2:20 They have to be totally liable, they’ve said. And they’re not being
allowed to sell them either under new laws. They can only lease them. But they
have got to be completely liable for any accidents, because what happens if a
car was parking, for instance. It’s got sensors on it, parking, a child steps in
front of the car. A truck comes past with a lot of mud on it, throws the mud
over the sensors as it’s driving past, and the car hits the child and kills them.
Who’s responsible? Is it the truck driver? Is it the manufacturer? Is it the driver
should keep the sensors clean? So, there’s a whole string of problems there, it’s
not settled yet.
2:58 One of the big concerns for me at the moment is the use of robots in
military. And you have lots of different kinds. You’ve got bomb disposal robots,
which are very good for protecting our soldiers. And you have robots that are
remote controlled, such as the drones that are flying around, although they are
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