Classical robotics competitions like Micromouse are working with non-teleoperated robots. That means, the robot is taking the next decision only by artificial intelligence. The reason why is because the AI engineers are trying to increase the productivity level. The assumption is, that a human operator doesn't make sense, because it will produce a high workload to that operator.
Non teleoperation has a major disadvantage. If all the decisions of the robot are controlled by software, the software needs to become very elaborated to solve a task. The problem is, that how to program an advanced AI is unknown, so what is available in reality is, that the difficulty of the robot competition gets smaller, so that current AI software is able to solve it. That means, the micromouse in the challenge doesn't need to do complex tasks, but it's enough if the mouse finds a way through the maze.- This reduced complexity allows the participant of the competition to program an AI Software which can do the task autonomously.
The problem is, that in reality the tasks have a higher complexity. A problem in which a robot has to travel through a maze isn't available in a real factory. Therefor the programmed software is useless. It will only be successful in synthetic robotics competition but not for practical applications. The gap between a robotic challenge and replacing real humans at the workplace is too large.
The reason why the gap is there has to do with the autonomous paradigm. All robotics competition have in common that the robot is controlled by software but not with a teleoperator. That means, the decision of the robot has to be calculated by the AI software. It's not possible to cheat the challenge, because it is doublechecked, if the onboard software is really able to control the robot. The requirement, that only software controlled are allowed to participant at the challenge is asking for a certain sort of robot. In case of the micromouse challenge, the typical software contains of a hierachical pathplanner in which high level trajectory is calculated together with low level motion commands. Such a pathplanner is optimized for the micromouse challenge. It's the opposite of a human level AI.
Somebody may argue, that it's not possible to program a human level AI which can solve more than a single game. And he is right, because such software is out of the scope of current AI. nobody knows, how to program a software which can play all the games and is able to communicate in natural language. The consequence is, that no alternative to a normal robotics competition is available.
Robotics competitions
The rules of challenge result into certain styles how to solve the issue. A typical example is a line following challenge. The task is to program a robot which can follow the line. The robot who is ooing so in the fastest amount of time has won. The problem with a line following challenge is, that it's not a robot task itself, but it's a programming problem. It can be solved by implementing an algorithm in the Python language. At first, the sensor information are read, then the robot has to calculate the next movement. A typical program for solving this task is working with the pid control method. That means, the sensor readings of the line is taken as input for the steering controller.
The problem is, that even a robot has won the challenge the robot project has failed. The created software can't be used outside the competition. It's a synthetic competition which provides a sandbox in which the programmer should proof if they have understood what a pid control algorithm is. The more elaborated form of a robotics challenge is an open problem which is equal to practical applications of robots in the reality.
It's important to know, that synthetici robotics challenges like a line following task and the requirement in the reality have nothing to do with each other. A line following problem is a game which is working by it's own rules. These rules are created with the purpose to simplify the problem. In contrast, using a robot in reality, for example in a factory or as a self-driving car is working by different rules.
The requirement in the reality can be summarized to “human level AI”. If a robot should replace human worker, the robot needs the same skills like a human. That means, the minimum requirement is, that the AI of the robot will pass the turing test. For sure, this requirement is a bit too high, no robot today will fulfill such a requirement. That means, a robot is not able to do useful tasks in the reality, because he failed in the turing test.
The problem is not described very well in the literature, so it make sense to focus on the detail. A naive assumption is, that practical applications of robots can be realized without providing human level AI. The hope is, that a normal AI algorithm which includes sensor perception and a bit of pathplanning is enough to solve tasks form the reality. The assumption is, that an industrial robot has to solve only simple tasks but there is no need to equip the robot with a massive human level AI which will pass the turing test.
This kind of assumption was never tested in the reality. It's the hope of the engineers because they can only program narrow AI system and the hope is, that these narrow AI systems can solve tasks from the reality. The altenative description is, to assume, that these narrow AI systems will fail in the task. That means, the robot which was programmed with some algorithm is not able to replace human workers and it can't be used in the reality. There are many facts available which are showing that this pessimistic thesis is correct.
All the robotics projects from the past have two things in common. First they are working with narrow AI but not with human level AI and secondly they failed. That means, the robot was not able to replace human workers. And between two facts there is a strong connection. That means, the robot projects failed because the robot was not able to pass the turing test.
Artificial Intelligence has a lot to do with solving games. Creating an AI controller is equal to solving a game. The open question is, which kind of games are available. An easy to solve game is TicTacToe. Such a game can be solved with a narrow AI. A basic strategy would be a gametree search. The problem is, that games played in reality are much harder to solve. For a certain standpoint, human workers are solving games too. If a cook prepares a meal he has to solve a game. This games contains of manipulation tasks with the hand and it has to do with decision making which kind of ingredients are needed. All the games in reality are too complex for current AI. That means, it's not possible to create an AI which can play games in the reality. The problem is, that games in the reality are fixed. It's not possible to simplify the existing games. So the answer is to increase the ability of the robot to a human level.
December 20, 2019
Comparison between Teleoperation and normal AI projects
- AI controlled robot: evaluated in robotics challenges like Micromouse, Narrow AI, sub-human level AI, a program is controllling the robot
- Teleoperated robot: no robotics challenge available yet, Human level skills because the human operator understands the situation as default, is not controlled by software but a human is in the loop
A software controlled robot has the maximum productivity. The machine is working similar to a electric motor: after activating the program no human intervention is needed. This reduces the costs to zero. The disadvantage is, that the robot's skills are restricted. Most software controlled robots are only able to do simple line following tasks and play games like chess.
In contrast, a teleoperated robot has the ability to solve all sorts of problems. Especially tasks from the reality which are asking of human level skills. A human controlled robotarm is able to pick&place objects as default without addtional software. The disadvantage of teleoperation is, that a human operator is needed all the time. The productivity is not higher than without any robot. The reason why teleoperation is used in reality is because the human operator can be connected over the internet. A typical example is telemedicine,in which the expert doctor is located far away from the operation room.
A contest for teleoperated robots looks the same like an RC car challenge. That means, each operator stands with a remote control in front of the robot and should do some tasks with it.
- Teleoperated robot: no robotics challenge available yet, Human level skills because the human operator understands the situation as default, is not controlled by software but a human is in the loop
A software controlled robot has the maximum productivity. The machine is working similar to a electric motor: after activating the program no human intervention is needed. This reduces the costs to zero. The disadvantage is, that the robot's skills are restricted. Most software controlled robots are only able to do simple line following tasks and play games like chess.
In contrast, a teleoperated robot has the ability to solve all sorts of problems. Especially tasks from the reality which are asking of human level skills. A human controlled robotarm is able to pick&place objects as default without addtional software. The disadvantage of teleoperation is, that a human operator is needed all the time. The productivity is not higher than without any robot. The reason why teleoperation is used in reality is because the human operator can be connected over the internet. A typical example is telemedicine,in which the expert doctor is located far away from the operation room.
A contest for teleoperated robots looks the same like an RC car challenge. That means, each operator stands with a remote control in front of the robot and should do some tasks with it.
Can teleoperation increase the productivity?
A major concern in the economy literature is adressed by the term of “productivity paradox”. It means, that the productivity hasn't increased with the advent of robotics at the workplace and in the worst case it will become lower with the introduction of robotics. From an economic perspective the productivity is a very important measurements, it has to do with how much the company has to spend to produce a product.
A possible technology which will result into powerful robots is teleoperation. Teleoperation is opposite from classical Artificial Intelligence because the idea is that a human operator is needed. The only new thing about teleoperation is, that the human operator can be located everywhere. From a productivity standpoint it's possible to guess what will happen with the productivity. It will remain the same.
That means, if 10 human workers are replaced by 10 robots and for each robot one human operator is needed in the loop the overall costs are the same. Or a bit higher, because the robotics hardware produces additional costs. So it's a zero sum game, isn't it? It's true that the productivity itself remains unchanged. A human controlled robot will have the same or a slower speed than a normal human. And the promise of robots to replace the human wasn't fulfilled.
So why exactly should a company give the technology a chance? I don't know. Perhaps the idea is, that teleoperation has non-measurable effects or another reason is, that the companies likes to robotics on the workplace but didn't want to wait until human level AI is available, so they are doing a step in between and using human operators in the loop. In the case of autonomous cars, the advantages of teleoperation are easier to grasp. Most today's cars are operating less than 5% of the hours per day. The average car is parking all the time, and no sharing takes place. With teleoperated cars, the situation can change drastically. This would allow – in theory – to let a single car drive 24/7 and less cars are needed overall. The pairing between cars, human operators and customers can be managed more flexible than with existing cars.`
household robot
A different application for a teleoperated robot is a household robot for the elderly. The idea is, that the robot is controlled by family members. That means, the task isn't solved by an AI, but a human is needed.
The underlying assumption is the same like for self-driving cars. The idea is, that only human level AI can replace human workers. It's not possible to remove a human from the loop or increase the productivity. The only thing what technology can provide is to increase the distance between the request for work and the human operator who provides the work. That means, a robot for the elderly is different from a normal machine. It has more in commong with an advanced telephone which also needs somebody on the other side.
A possible technology which will result into powerful robots is teleoperation. Teleoperation is opposite from classical Artificial Intelligence because the idea is that a human operator is needed. The only new thing about teleoperation is, that the human operator can be located everywhere. From a productivity standpoint it's possible to guess what will happen with the productivity. It will remain the same.
That means, if 10 human workers are replaced by 10 robots and for each robot one human operator is needed in the loop the overall costs are the same. Or a bit higher, because the robotics hardware produces additional costs. So it's a zero sum game, isn't it? It's true that the productivity itself remains unchanged. A human controlled robot will have the same or a slower speed than a normal human. And the promise of robots to replace the human wasn't fulfilled.
So why exactly should a company give the technology a chance? I don't know. Perhaps the idea is, that teleoperation has non-measurable effects or another reason is, that the companies likes to robotics on the workplace but didn't want to wait until human level AI is available, so they are doing a step in between and using human operators in the loop. In the case of autonomous cars, the advantages of teleoperation are easier to grasp. Most today's cars are operating less than 5% of the hours per day. The average car is parking all the time, and no sharing takes place. With teleoperated cars, the situation can change drastically. This would allow – in theory – to let a single car drive 24/7 and less cars are needed overall. The pairing between cars, human operators and customers can be managed more flexible than with existing cars.`
household robot
A different application for a teleoperated robot is a household robot for the elderly. The idea is, that the robot is controlled by family members. That means, the task isn't solved by an AI, but a human is needed.
The underlying assumption is the same like for self-driving cars. The idea is, that only human level AI can replace human workers. It's not possible to remove a human from the loop or increase the productivity. The only thing what technology can provide is to increase the distance between the request for work and the human operator who provides the work. That means, a robot for the elderly is different from a normal machine. It has more in commong with an advanced telephone which also needs somebody on the other side.
December 19, 2019
Teleoperation is a here to stay
Sometimes, Teleoperation is introduced as bridge technology for creating Artificial Intelligence. The idea is, that Teleoperation is much easier to realize than a fully autonomous system. Most engineers who have implemented Teleoperation are trying to increase the automation level further, because they want to remove the human operator from the loop. But is this vision realistic? After removing the human operator from the loop, the robot will loose it's human level AI capabilities. It's no longer possible to talk to the robot with natural language. For filling the gap of the human an advanced sort of artificial Intelligence has to be realized.
It's a well known fact, that a human level AI is not available. And it won't be available in the next 50 years. After removing the human operator the robot will have a much smaller cognitive capabilities. The naive assumption is, that a state-of-the-art AI can control the robot by it's own, and that the task doesn't require the full human level skills. But this kind of assumption is wrong.
The reason why software controlled robots are not used in reality is because all the tasks are asking for human level skills. It's not possible to control airplanes, cars, ships, drones or grasping robot with sub-human-level AI. Let us ask the engineers in which timespan human level AI in software becomes realistic. The answer is, that they don't know. What was demonstrated in robotics challenges is only a narrow AI. That is a software which drives a robot on a line, or which can do simple image recognition tasks. The engineers have no idea how to program a human level AI. That means, they are not able to provide such software. And because of this reason, it's not possible to remove a human operator from the loop. Teleoperation is the only working robot which is available.
From a fantasy point of view, it's possible that one day, human level AI is available. That is a piece of software which can do everything what a human has to offer. It includes understanding of natural language, detecting all sorts of objects, learn new things and provides empathy. In some science fiction movies, Human level AI systems were shown, for example Data from Star Trek TNG. But right now no such thing is available in reality. Therefore AI can't be realized.
What makes teleoperation so amazing is, that without complicated software, human level AI is provided as default. All what the human operator needs is a joystick and monitor and the robot is able to do everything what the human can do. It's not a real AI, because there is a human in the loop. But the robot is on the same level like a human. He can be used for practical applications.
Practical example
I'm not the first one who is arguing pro Teleoperated robots. The company “Phantom Auto” has developed a teleoperated self-driving car. The basic idea is, that a car needs at minimum human level AI, which means, that without a human driver a car is not allowed to drive in real traffic.
The interesting fact is, that self-driving car engineers in the past have argued a different way. The assumption was, that the robot car can be controlled with software and they have demonstrated it in synthetic driving challenges. Either for RC Cars which were controlled by Python script or with real cars. The problem is, that a software controlled car provides only sub-human level skills. It is able to do some tasks like path planning and automatic steering but it won't understand a simple sentence like “hello robot, what's up?”.
The main reason why robots are not available is because human level AI is the minimum for practical application. And what the company Phantom Auto is doing is to provide such feature. A teleoperated car is working on a human level. It will understand the mentioned sentence and will respond to it. This make it a good choice for practical applications.
The interesting point is, that a teleoperated car provides a lot of technology except Artificial Intelligence. The onboard computer provides only a connection to a remote location, but the computer is not able to control the car by it's own.
It's a well known fact, that a human level AI is not available. And it won't be available in the next 50 years. After removing the human operator the robot will have a much smaller cognitive capabilities. The naive assumption is, that a state-of-the-art AI can control the robot by it's own, and that the task doesn't require the full human level skills. But this kind of assumption is wrong.
The reason why software controlled robots are not used in reality is because all the tasks are asking for human level skills. It's not possible to control airplanes, cars, ships, drones or grasping robot with sub-human-level AI. Let us ask the engineers in which timespan human level AI in software becomes realistic. The answer is, that they don't know. What was demonstrated in robotics challenges is only a narrow AI. That is a software which drives a robot on a line, or which can do simple image recognition tasks. The engineers have no idea how to program a human level AI. That means, they are not able to provide such software. And because of this reason, it's not possible to remove a human operator from the loop. Teleoperation is the only working robot which is available.
From a fantasy point of view, it's possible that one day, human level AI is available. That is a piece of software which can do everything what a human has to offer. It includes understanding of natural language, detecting all sorts of objects, learn new things and provides empathy. In some science fiction movies, Human level AI systems were shown, for example Data from Star Trek TNG. But right now no such thing is available in reality. Therefore AI can't be realized.
What makes teleoperation so amazing is, that without complicated software, human level AI is provided as default. All what the human operator needs is a joystick and monitor and the robot is able to do everything what the human can do. It's not a real AI, because there is a human in the loop. But the robot is on the same level like a human. He can be used for practical applications.
Practical example
I'm not the first one who is arguing pro Teleoperated robots. The company “Phantom Auto” has developed a teleoperated self-driving car. The basic idea is, that a car needs at minimum human level AI, which means, that without a human driver a car is not allowed to drive in real traffic.
The interesting fact is, that self-driving car engineers in the past have argued a different way. The assumption was, that the robot car can be controlled with software and they have demonstrated it in synthetic driving challenges. Either for RC Cars which were controlled by Python script or with real cars. The problem is, that a software controlled car provides only sub-human level skills. It is able to do some tasks like path planning and automatic steering but it won't understand a simple sentence like “hello robot, what's up?”.
The main reason why robots are not available is because human level AI is the minimum for practical application. And what the company Phantom Auto is doing is to provide such feature. A teleoperated car is working on a human level. It will understand the mentioned sentence and will respond to it. This make it a good choice for practical applications.
The interesting point is, that a teleoperated car provides a lot of technology except Artificial Intelligence. The onboard computer provides only a connection to a remote location, but the computer is not able to control the car by it's own.
Robotics and the invention of neoluddism
On the first look, humanoid robotics is the forefront of technological progress. Building a biped robot who can do a task is equal to introduce futuristic technology to the world. The interesting point is, that especially advanced robotics is providing an anti-technology standpoint. The reason is, that a robotics project contains of two elements. First the robot itself and secondly, the explanation about the robot. The second part of the system can be called neoluddism because it won't bring the world forward but it is spreading misinformation.
Let us go into the details, how humanoid robotics are explained in the literature and in videos. In most cases, it's described as a successful project. The humanoid robot walks through the house and is doing useful tasks for example cleaning the kitchen. The audience gets the impression, that the robot is a product which will increase the productivity in reality. What makes the story problematic is, that no alternative is presented. The audience has no opportunity to validate if the robot is useful in reality or not.
To make the bottleneck clear it's important to tell a different kind of story. Suppose, there is a practical joke, which is a machine who can't provide anything. And the story is, that this non-sense machine will become a useful product. If the story is told the right way, the audience will laugh about it, because it makes no sense at all. Do the people laugh if they read stories about household robots? No they don't because the plot prevents that the audience gets the full impression. If the audience is not allow to laugh about the product it gets indoctrinated.
Laughing is equal to freedom. It allows somebody to stand above a subject. Telling a joke is equal to spread the truth. In case of humanoid robotics the amount of jokes is rare. That means, that there is no intention to explain what a robot is really doing. And the user is fooled with misinformation.
Productivity
There is a reason why the productivity of robotics is incredible low. By self-definition a robot is trying to replace the control part of a system with automated algorithm. The robot isn't working like a classical industrial machine but the robot is using sensors and actuators to decide something. The car is driven by the motor, and the robot is controlling the wheel of the car. The crane is driven with electric current but the robotics crane operator controls the buttons.
Unfortunately this part is hard or even impossible to automate. Most robots are working great from the technical side. But they fail in doing the sensor-actuator task in a meaningful way. The work hypothesis is, that only human level Artificial Intelligence is able to replace human workers. Right now, no human level AI is available and as a consequence robots have to fail in increasing the productivity.
The problem is located in missing research about failed industrial robots in the past. Many attempts were made over the decades. But the amount of productivity was never measured. If a company who has sold industrial robots went into bankruptcy it's ignored by the robotics community. They pretend, the case was never there. Instead they are talking about future robots which are more powerful.
Let us go into the details, how humanoid robotics are explained in the literature and in videos. In most cases, it's described as a successful project. The humanoid robot walks through the house and is doing useful tasks for example cleaning the kitchen. The audience gets the impression, that the robot is a product which will increase the productivity in reality. What makes the story problematic is, that no alternative is presented. The audience has no opportunity to validate if the robot is useful in reality or not.
To make the bottleneck clear it's important to tell a different kind of story. Suppose, there is a practical joke, which is a machine who can't provide anything. And the story is, that this non-sense machine will become a useful product. If the story is told the right way, the audience will laugh about it, because it makes no sense at all. Do the people laugh if they read stories about household robots? No they don't because the plot prevents that the audience gets the full impression. If the audience is not allow to laugh about the product it gets indoctrinated.
Laughing is equal to freedom. It allows somebody to stand above a subject. Telling a joke is equal to spread the truth. In case of humanoid robotics the amount of jokes is rare. That means, that there is no intention to explain what a robot is really doing. And the user is fooled with misinformation.
Productivity
There is a reason why the productivity of robotics is incredible low. By self-definition a robot is trying to replace the control part of a system with automated algorithm. The robot isn't working like a classical industrial machine but the robot is using sensors and actuators to decide something. The car is driven by the motor, and the robot is controlling the wheel of the car. The crane is driven with electric current but the robotics crane operator controls the buttons.
Unfortunately this part is hard or even impossible to automate. Most robots are working great from the technical side. But they fail in doing the sensor-actuator task in a meaningful way. The work hypothesis is, that only human level Artificial Intelligence is able to replace human workers. Right now, no human level AI is available and as a consequence robots have to fail in increasing the productivity.
The problem is located in missing research about failed industrial robots in the past. Many attempts were made over the decades. But the amount of productivity was never measured. If a company who has sold industrial robots went into bankruptcy it's ignored by the robotics community. They pretend, the case was never there. Instead they are talking about future robots which are more powerful.
Human Level AI for industrial robots
Industrial robots were never successful because all the tasks in reality are asking for human level AI. A human level AI is a robot which is on the same level like a human worker. That means, he understands normal English, is able to learn new tasks and is able to fulfill complex tasks by it's own. What autonomous robots can provide is reduced form of Artificial Intelligence. The typical AI control program is able to steer a robot on a line or can do simple pick&place tasks which are preprogrammed by the algorithm. In robotics challenges like micromouse and robocup such minimal AI is enough to solve the task. The problem is, that in reality the robot needs more skills to become highly productive.
From a technical point of view, it's not possible to program a human level AI in software. Even advanced research projects in the universities are not providing such features. All the existing robots have only sub-human level AI implemented. Because of this reason, they failed in real life applications. The better alternative is to use a teleoperated robot. Teleoperation means, that a human operator controls the robot which is connected with the robot with an internet connection. Teleoperation itself is not able to increase the productivity. The human operator will need the same time until the task is finished, and he has to be payed like the normal worker. The advantage of teleoperation is, that the distance between the robot and the human operator can be increased.
The normal Internet connection is remarkable fast. It allows to control a robot in realtime, similar to what a multi-player online game is about. That means the latency in games is enough for a robot control problem. In contrast to autonomous robots, a teleoperated robot is on the same cognitive level like a human. That means, it's possible to talk to the machine like “hello robot”. And the robot will answer in normal English, because on the other side there is normal human.
This kind of human level skills is required for solving real tasks. For example, the crane on a construction site is doing a complex task and there is a need to talk to the crane operator. If the crane operator is a software which was programmed for pick&place actions, it's not possible to talk to the crane. As a result, autonomous cranes are not used in reality. But a teleoperated crane is useful tool. The same is true for delivery robots which transports a box from a to b. A normal robot which is working with a computer program doesnt provide human level capabilities. A simple request like “put the box down” won't be understood by the robot, because the software has no speech recognition module. But if the same delivery drone is controlled a by a human operator it will understand each single word. And much better, the human operator will understand even sign language without extra commands so that the interaction make sense.
The work hypothesis is, that teleoperated robots are useful for commercial applications while autonomous robots are not. The only task which can be solved by software controlled robots are synthetici challenges like Micromouse, but these challenges are different from practical applications.
Is there a need for human level AI?
Perhaps it make sense to go a step backword and describe the precondition for normal robotics. The common idea of robot programming is, that at first the robot is equipped with piece of software, and then the software is able to solve the task. A typical example is a pick&place robot which moves an object from A to B. The assumption is, that the pick&place software is enough for solving problems in reality.
The problem is, that the engineers are not able to increase the skills of the software but what they are doing in reality is to modify the requirements of the tasks. In case of the pick&place robot the engineers are inventing a robot challenge in which a box needs to be moved from A to B. If the robot is able to do so, he has won the challenge. This kind of task is very different from real applications. In reality, a pick&place task is more complicated. This sort of real tasks can't be solved by the initial software. That is the reason, why a pick&place robots works great in the laboratory but fails in the reality. Let us imagine a real pick&place task which is required in the factory. Solving this task with a robot is not possible. What the companies are doing is to utilize human workers for this task. So the question is: which kind of software is needed to replace a human worker with an AI?
The answer is a bit complicated. It has to do with the task. Or let me reformulate the question: how much Artificial Intelligence is needed to solve pick&place tasks from the reality? The answer is, that only human level AI is capable of doing so. Even if the task looks easy to solve a normal algorithm isn't able to do so. That is the true reason why robotics were never used in the factory. Because the gap what robots have to offer and the requirement of the factory is, is too large.
The problem is not located in the domain of Artificial Intelligence. But it has to do with the human work in reality. All the jobs in the service industry, on the construction site, in the supermarket and for driving trucks to a destination are highly complex. They look easy only for humans, but they too complicated for robots. The reason why these tasks are so demanding is because most of the work was automated already. For example the engine in the truck moves the vehicle forward and the engine is driven by fuel. The only thing what is not automated is the steering task, which means to operate the truck and decide in which moment the brake is needed. The same is true for the crane on a construction site. The crane itself is driven by an electric motor. What the human operator is doing is to control the crane. That means, he is doing a high level task which needs a lot of domain specific knowledge.
This kind of human level knowledge isn't provided by simple path planning algorithm. The minimum requirement for a human worker is, that he understands normal English. Nearly all existing robots are not able to do so, only humans can understand a sentence like “please stop the engine”. If a robot doesn't even understand a simple sentence, how is he able to replace the human worker? Right, there is no way and as a result the automation project will fail.´
From a technical point of view, it's not possible to program a human level AI in software. Even advanced research projects in the universities are not providing such features. All the existing robots have only sub-human level AI implemented. Because of this reason, they failed in real life applications. The better alternative is to use a teleoperated robot. Teleoperation means, that a human operator controls the robot which is connected with the robot with an internet connection. Teleoperation itself is not able to increase the productivity. The human operator will need the same time until the task is finished, and he has to be payed like the normal worker. The advantage of teleoperation is, that the distance between the robot and the human operator can be increased.
The normal Internet connection is remarkable fast. It allows to control a robot in realtime, similar to what a multi-player online game is about. That means the latency in games is enough for a robot control problem. In contrast to autonomous robots, a teleoperated robot is on the same cognitive level like a human. That means, it's possible to talk to the machine like “hello robot”. And the robot will answer in normal English, because on the other side there is normal human.
This kind of human level skills is required for solving real tasks. For example, the crane on a construction site is doing a complex task and there is a need to talk to the crane operator. If the crane operator is a software which was programmed for pick&place actions, it's not possible to talk to the crane. As a result, autonomous cranes are not used in reality. But a teleoperated crane is useful tool. The same is true for delivery robots which transports a box from a to b. A normal robot which is working with a computer program doesnt provide human level capabilities. A simple request like “put the box down” won't be understood by the robot, because the software has no speech recognition module. But if the same delivery drone is controlled a by a human operator it will understand each single word. And much better, the human operator will understand even sign language without extra commands so that the interaction make sense.
The work hypothesis is, that teleoperated robots are useful for commercial applications while autonomous robots are not. The only task which can be solved by software controlled robots are synthetici challenges like Micromouse, but these challenges are different from practical applications.
Is there a need for human level AI?
Perhaps it make sense to go a step backword and describe the precondition for normal robotics. The common idea of robot programming is, that at first the robot is equipped with piece of software, and then the software is able to solve the task. A typical example is a pick&place robot which moves an object from A to B. The assumption is, that the pick&place software is enough for solving problems in reality.
The problem is, that the engineers are not able to increase the skills of the software but what they are doing in reality is to modify the requirements of the tasks. In case of the pick&place robot the engineers are inventing a robot challenge in which a box needs to be moved from A to B. If the robot is able to do so, he has won the challenge. This kind of task is very different from real applications. In reality, a pick&place task is more complicated. This sort of real tasks can't be solved by the initial software. That is the reason, why a pick&place robots works great in the laboratory but fails in the reality. Let us imagine a real pick&place task which is required in the factory. Solving this task with a robot is not possible. What the companies are doing is to utilize human workers for this task. So the question is: which kind of software is needed to replace a human worker with an AI?
The answer is a bit complicated. It has to do with the task. Or let me reformulate the question: how much Artificial Intelligence is needed to solve pick&place tasks from the reality? The answer is, that only human level AI is capable of doing so. Even if the task looks easy to solve a normal algorithm isn't able to do so. That is the true reason why robotics were never used in the factory. Because the gap what robots have to offer and the requirement of the factory is, is too large.
The problem is not located in the domain of Artificial Intelligence. But it has to do with the human work in reality. All the jobs in the service industry, on the construction site, in the supermarket and for driving trucks to a destination are highly complex. They look easy only for humans, but they too complicated for robots. The reason why these tasks are so demanding is because most of the work was automated already. For example the engine in the truck moves the vehicle forward and the engine is driven by fuel. The only thing what is not automated is the steering task, which means to operate the truck and decide in which moment the brake is needed. The same is true for the crane on a construction site. The crane itself is driven by an electric motor. What the human operator is doing is to control the crane. That means, he is doing a high level task which needs a lot of domain specific knowledge.
This kind of human level knowledge isn't provided by simple path planning algorithm. The minimum requirement for a human worker is, that he understands normal English. Nearly all existing robots are not able to do so, only humans can understand a sentence like “please stop the engine”. If a robot doesn't even understand a simple sentence, how is he able to replace the human worker? Right, there is no way and as a result the automation project will fail.´
December 18, 2019
Switch off the robot and and have fun -- Analyzing the bottleneck in modern automation technology
From a technical perspective, Artificial Intelligence research has developed algorithm for controlling robots. The most advanced one are motion planning with model predictive control. The idea is to create a forward model of the system and use the model for trajectory planning. This allows to build biped robots and manipulation robot hands.
Somebody may argue, that the practical applications are obvious. Because it's possible to utilize the technique in self-driving cars and in pick&place robots which can be used for industrial applications. There is only a smaller problem. It seems, that solving a robotics task from a technical application is not enough. That means, on the one hand it's possible to build a pick&place robot and on the other side it's not possible.
What can be solved with modern AI techniques easily are so called robotics challenges. That are synthetic challenges like micromouse, robocup, Mario AI or robot pick&place tasks. The mentioned combination of a forward model, motion planning and model predictive control results into a working system. The problem is, that a synthetic robot challenge is very different from a practical application of a robot. A practical application is equal to convert the technology into a product, and sell it to customers. Exactly this is not possible and attempts from the past into that direction have failed.
What does that mean? Creating a robot with the help of model predictive control and planning is the best practice method for building a pick&place robot which works in a challenge. Using the same technique to build a commercial robot which is sold on the market will fail. That means, the same technique is a powerful one and a useless one at the same time. This paradox is hard to grasp. Mostly it's assumed, that artificial Intelligence is a technical challenge. That means, if the algorithm was identified to control a robot than the overall problem is solved.
To measure the bottleneck in reality it's not enough to describe robotics from a technical perspective. The more elaborated form is to investigate the history of robotics companies and their failed attempt in selling a product to customers. The good news is, that in the last decade many examples are visible. The interesting question is why a certain robotics company have failed to sell the product. There are some arguments available:
- price of the product is too high. This was the case for the PR2 robot which costs around 400k US$
- robot technology is not advanced enough. This was the case for the helpmate robot in the 1990s. In that time, the onboard computer was slow and the sensors were not accurate.
- price is low and robot technology is advanced but the customer doesn't buy the product too, this was the case for the Baxter robot from rethink robotics
The pessimistic prediction is, that even the robot is sold for little money and is working with the latest hardware and software the customer won't buy the product. This outlook is equal to a general failure of robotics. Which means, that it's not possible overall to sell a robot to customers. Let us construct a hypothetical example. Suppose a company builds a pick&place robot which is very cheap and is working with model predictive control. Will this product become successful on the market or not?
The prediction is, that the robot won't find it's customers. The reason is, that even a lowcost, MPC-based robot is not able to increase the productivity in a real life condition. The task which is solved by the robot, and the requirements in reality are not the same. Or let me give another example which is in the domain of most people.
Suppose, a car company develops a self-driving car for the same price like a normal car. It's equipped with the latest sensor technology and advanced AI software. Technically spoken the car is able to drive autonomously. Will this car get customers or not? The naive prediction is, that such a car will get sold many million times worldwide. Because it reduces the workload of all the human drivers. It is useful for private households and commercial applications as well.
The problem is, that such an optimistic assumption is maybe wrong. In reality, a self-driving car is not fulfilling the real requirements. It won't reduce the workload of the driver but it increases the workload. That means, the human driver will deactivate the autopilot if he likes to relax a bit. This kind of counter-intuitive strategy isn't showing a missing knowledge of the human driver but it shows, that something with robotics technology is wrong.
Autopilots in ships
An interesting example of automated controlled vehicles is a ship. Autopilots for ships are available for decades, or at least it is written in the literature that such autopilots are available. A more realistic investigation comes to the conclusion that 0% of all ships today are using an autopilot. In contrast, all the miles were driven manual without computerized decision support. How can this mismatch be explained?
It's important to separate between autopilots in ships and the literature about autopilots. What is explained in the literature is the technical working of an autopilot. In the average book it's mentioned that steering a ship is a mathematical challenge. In some newer publication it's described as a problem for control theory which can be solved with modern algorithms. It's possible to make this problem more obvious by developing an autopilot from scratch and compare different RC controlled ships in a challenge.
On the other hand, there is the problem of ship steering in reality. In reality, the autopilot is something which isn't available. A ship is working with a human operator in the loop. The operator has some buttons he is able to press and before he can do so, he has to ask the captain what he likes to do next. An autonomous controlled ship would be equal to replace human work with software. This futuristic vision is not available in the reality, and it won't happen in the next 50 years. In reality, the amount of human operators on the bridge is constant. That means today ships are controlled the same way, like in the 1950s.
Sure, the technology has evolved a little bit. Today's ships are using computers and modern sensors. But the productivity is the same. Productivity is the measurements how many human workers are needed to control a ship with a certain size. The productivity has never increased. That means, computer technology was not able to replace human work with algorithms.
The surprising fact is, that even remote controlled ships are not available right now. It's a vision for the future to increase the efficiency for freight transport. The idea is, that if the human operator can stay outside of the ship it's easier to control the device. In case of a remote controlled ship, the amount of needed humans in the loop remains the same. The only advantage is, that they doesn't need to by physical on the ship. This kind of low end automation was never realized because the disadvantage is, that many new sensors and costly equipment has to be installed on the ship. That means, even in the year 2019, the amount of remote control freight ships is 0%. The prediction is, that in the next 50 years the amount will be the same.
Conclusion: Autopilot for ships are not available. Remote controlled ships are not available. The productivity over the last 50 years hasn't increased and it's not possible to use Autopilots in the reality. What is written in the book about Artificial Intelligence is wishful thinking.
Somebody may argue, that the practical applications are obvious. Because it's possible to utilize the technique in self-driving cars and in pick&place robots which can be used for industrial applications. There is only a smaller problem. It seems, that solving a robotics task from a technical application is not enough. That means, on the one hand it's possible to build a pick&place robot and on the other side it's not possible.
What can be solved with modern AI techniques easily are so called robotics challenges. That are synthetic challenges like micromouse, robocup, Mario AI or robot pick&place tasks. The mentioned combination of a forward model, motion planning and model predictive control results into a working system. The problem is, that a synthetic robot challenge is very different from a practical application of a robot. A practical application is equal to convert the technology into a product, and sell it to customers. Exactly this is not possible and attempts from the past into that direction have failed.
What does that mean? Creating a robot with the help of model predictive control and planning is the best practice method for building a pick&place robot which works in a challenge. Using the same technique to build a commercial robot which is sold on the market will fail. That means, the same technique is a powerful one and a useless one at the same time. This paradox is hard to grasp. Mostly it's assumed, that artificial Intelligence is a technical challenge. That means, if the algorithm was identified to control a robot than the overall problem is solved.
To measure the bottleneck in reality it's not enough to describe robotics from a technical perspective. The more elaborated form is to investigate the history of robotics companies and their failed attempt in selling a product to customers. The good news is, that in the last decade many examples are visible. The interesting question is why a certain robotics company have failed to sell the product. There are some arguments available:
- price of the product is too high. This was the case for the PR2 robot which costs around 400k US$
- robot technology is not advanced enough. This was the case for the helpmate robot in the 1990s. In that time, the onboard computer was slow and the sensors were not accurate.
- price is low and robot technology is advanced but the customer doesn't buy the product too, this was the case for the Baxter robot from rethink robotics
The pessimistic prediction is, that even the robot is sold for little money and is working with the latest hardware and software the customer won't buy the product. This outlook is equal to a general failure of robotics. Which means, that it's not possible overall to sell a robot to customers. Let us construct a hypothetical example. Suppose a company builds a pick&place robot which is very cheap and is working with model predictive control. Will this product become successful on the market or not?
The prediction is, that the robot won't find it's customers. The reason is, that even a lowcost, MPC-based robot is not able to increase the productivity in a real life condition. The task which is solved by the robot, and the requirements in reality are not the same. Or let me give another example which is in the domain of most people.
Suppose, a car company develops a self-driving car for the same price like a normal car. It's equipped with the latest sensor technology and advanced AI software. Technically spoken the car is able to drive autonomously. Will this car get customers or not? The naive prediction is, that such a car will get sold many million times worldwide. Because it reduces the workload of all the human drivers. It is useful for private households and commercial applications as well.
The problem is, that such an optimistic assumption is maybe wrong. In reality, a self-driving car is not fulfilling the real requirements. It won't reduce the workload of the driver but it increases the workload. That means, the human driver will deactivate the autopilot if he likes to relax a bit. This kind of counter-intuitive strategy isn't showing a missing knowledge of the human driver but it shows, that something with robotics technology is wrong.
Autopilots in ships
An interesting example of automated controlled vehicles is a ship. Autopilots for ships are available for decades, or at least it is written in the literature that such autopilots are available. A more realistic investigation comes to the conclusion that 0% of all ships today are using an autopilot. In contrast, all the miles were driven manual without computerized decision support. How can this mismatch be explained?
It's important to separate between autopilots in ships and the literature about autopilots. What is explained in the literature is the technical working of an autopilot. In the average book it's mentioned that steering a ship is a mathematical challenge. In some newer publication it's described as a problem for control theory which can be solved with modern algorithms. It's possible to make this problem more obvious by developing an autopilot from scratch and compare different RC controlled ships in a challenge.
On the other hand, there is the problem of ship steering in reality. In reality, the autopilot is something which isn't available. A ship is working with a human operator in the loop. The operator has some buttons he is able to press and before he can do so, he has to ask the captain what he likes to do next. An autonomous controlled ship would be equal to replace human work with software. This futuristic vision is not available in the reality, and it won't happen in the next 50 years. In reality, the amount of human operators on the bridge is constant. That means today ships are controlled the same way, like in the 1950s.
Sure, the technology has evolved a little bit. Today's ships are using computers and modern sensors. But the productivity is the same. Productivity is the measurements how many human workers are needed to control a ship with a certain size. The productivity has never increased. That means, computer technology was not able to replace human work with algorithms.
The surprising fact is, that even remote controlled ships are not available right now. It's a vision for the future to increase the efficiency for freight transport. The idea is, that if the human operator can stay outside of the ship it's easier to control the device. In case of a remote controlled ship, the amount of needed humans in the loop remains the same. The only advantage is, that they doesn't need to by physical on the ship. This kind of low end automation was never realized because the disadvantage is, that many new sensors and costly equipment has to be installed on the ship. That means, even in the year 2019, the amount of remote control freight ships is 0%. The prediction is, that in the next 50 years the amount will be the same.
Conclusion: Autopilot for ships are not available. Remote controlled ships are not available. The productivity over the last 50 years hasn't increased and it's not possible to use Autopilots in the reality. What is written in the book about Artificial Intelligence is wishful thinking.
A pessimistic prediction for the future of robotics
There are two advanced robotics available, the Baxter robot from Rethink robotics company, and a self-driving car from different companies. Both systems are equipped with cutting edge software and they are able to fulfill certain tasks. The interesting fact is, that Baxter and self-driving cars have two sides which should be mentioned. On the first look, the project is amazing. In the case of the Baxter robot it's the first time, that an industrial robot can be programmed by everyone. The system is robust against errors and can be reprogrammed for different tasks. In contrast to other robots, Baxter is way more advanced.
Unfortunately, there is a less known part of the project. The rethink robotics company has went bankrupt last year because they were not able to sell the product to the market. And after watching some of the videos the reason why is obvious: the Baxter robot isn't solving practical tasks, but it's increases the complexity. That means, with the Baxter robot in the loop the costs will become higher and not cheaper. The same problem is there for self-driving cars. From a technical perspective, current autonomous cars are advanced. But they can't be used in reality.
The open question is why such a gap is there between the promise of the inventor and the reality. The problem has nothing to do with Artificial Intelligence itself, but it's located in the tasks which should be done by robots. In most cases, the promise is, that a robot can replace a human worker. For doing so, the robot needs the same capabilities as a human worker, and this is not the case. What self-driving cars and the Baxter robot have to offer is a computer program which is some sort of Narrow AI. It selves a certain task which was programmed before. This kind of capabilities is not enough to replace a human worker.
Some engineers will argue, that this is not a real problem, because current robotics is sold as a co-robot, which means, that the robot and the human are working together on the same problem. Exactly this is not available. A comparison between a) a single human and b) a human and a robot will show, that the single human is more efficient. He can do the same task in a lower amount of time.
Let us focus on the latest generation of autopilots which are available in some of luxuary cars. The surprising fact is, that if the human drivers activates the autopilot his workload will become higher, but not lower. That means, the autopilot isn't supporting the human but he puts the human under stress. The same is true for the Baxter robot. So the conclusion is, that Artificial Intelligence isn't a helpful tool but it's the opposite. Exactly of this reason, Rethink robotics has went into bankrupt.
Let us make a simple reality check. The amount of Youtube videos about the Baxter robot is amazing. Nearly all features are explained of how to use the machine. In contrast, not a single company is using the robot for practical applications. So the conclusion is, that Baxter is some kind of educational project but can't be utilized in reality. It seems, that the world has huge interest in explaining the Baxter robot to others, but there is nobody who is watching all these tutorials and use this knowledge. So the prediction is, that the knowledge is useless. It means, it's not possible to learn how to install a robot in a factory.
There is without a need for increased productivity in the econmy. But robots who are working are a dead end. If the idea is to increase the automation level, other options apart from Artificial Intelligence should be investigated first.
Unfortunately, there is a less known part of the project. The rethink robotics company has went bankrupt last year because they were not able to sell the product to the market. And after watching some of the videos the reason why is obvious: the Baxter robot isn't solving practical tasks, but it's increases the complexity. That means, with the Baxter robot in the loop the costs will become higher and not cheaper. The same problem is there for self-driving cars. From a technical perspective, current autonomous cars are advanced. But they can't be used in reality.
The open question is why such a gap is there between the promise of the inventor and the reality. The problem has nothing to do with Artificial Intelligence itself, but it's located in the tasks which should be done by robots. In most cases, the promise is, that a robot can replace a human worker. For doing so, the robot needs the same capabilities as a human worker, and this is not the case. What self-driving cars and the Baxter robot have to offer is a computer program which is some sort of Narrow AI. It selves a certain task which was programmed before. This kind of capabilities is not enough to replace a human worker.
Some engineers will argue, that this is not a real problem, because current robotics is sold as a co-robot, which means, that the robot and the human are working together on the same problem. Exactly this is not available. A comparison between a) a single human and b) a human and a robot will show, that the single human is more efficient. He can do the same task in a lower amount of time.
Let us focus on the latest generation of autopilots which are available in some of luxuary cars. The surprising fact is, that if the human drivers activates the autopilot his workload will become higher, but not lower. That means, the autopilot isn't supporting the human but he puts the human under stress. The same is true for the Baxter robot. So the conclusion is, that Artificial Intelligence isn't a helpful tool but it's the opposite. Exactly of this reason, Rethink robotics has went into bankrupt.
Let us make a simple reality check. The amount of Youtube videos about the Baxter robot is amazing. Nearly all features are explained of how to use the machine. In contrast, not a single company is using the robot for practical applications. So the conclusion is, that Baxter is some kind of educational project but can't be utilized in reality. It seems, that the world has huge interest in explaining the Baxter robot to others, but there is nobody who is watching all these tutorials and use this knowledge. So the prediction is, that the knowledge is useless. It means, it's not possible to learn how to install a robot in a factory.
There is without a need for increased productivity in the econmy. But robots who are working are a dead end. If the idea is to increase the automation level, other options apart from Artificial Intelligence should be investigated first.
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