Accomplishing DARPA's robot challenge (pt 2) using Human Level Artificial Intelligence

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DARPA, a defense agency of the US government has announced the Robot Challenge 2012 and the first contest will be held on Dec. of 2013. Their mission is to give money to Universities and technology companies to build them a robot that can do "mundane human tasks". According to the government, the reason for the the contest was to build robots that can do search and rescue missions. They wanted to build robots that can go into dangerous zones to do tasks. The Japan earthquake was one example.

There were several tasks (about 8 tasks) the human robot has to accomplish in the robot challenge. 1. walk around dangerous terrains. 2 open a locked door. 3. identify a broken pipe. 4. climb a ladder. 5. use tools, like a sledge hammer. 6. fix a broken pipe. 7. control vehicles with simple functions. 8. push buttons.

In the video, a robot was given a task to load 200 crates into delivery trucks using a forklift. The robot has exactly 5 hours to accomplish this task. I know that it doesn't require a lot of intelligence to operate a forklift, and that a 5 year old child can probably do this. However, there are things that only adults understand and operating forklifts has great responsibilities. If a person uses a forklift on a very heavy crate and the load exceeds the weight capacity, then the forks can break. This will cost the company thousands of dollars in repairs. If the propane tank is leaking fluids and the operator doesn't fix the problem, the forklift might blow up, killing the driver. Thus, there are responsibilities that are included in operating a forklift and only people who are skilled and certified should operate the machine.

There are no sound in the video because I wanted to show the viewers what the robot is thinking while accomplishing a mission. The flashing text and freeze frames are the internal thoughts of the robot and not instruction text for the viewers. These internal thoughts describe the details of how the robot produce intelligence.

In the video, the robot uses lessons in school to operate a forklift. Controlling a forklift is very easy because there are only a few controls. However, following standard procedures, safety inspections, and following rules while in operation is the most important thing. In order to operate a forklift, you need to build a human robot with 2 hands and 2 feet. Those autonomous forklifts in warehouses today are operated by software (2013). The advantage of using a human robot is that you can check parts of the vehicle for damages. The robot can check the tires to see if there is enough air pressure or check for damages. If the robot does find damages, he can replace the tire with his hands and feets. In addition, a human robot can control any type of forklift or machine (like a car or plane). You don't need to buy a specific type of autonomous forklift. The robot can drive a forklift built in the 1950's.

Learning to operate a forklift is done through lectures or books. Thus, no machine learning is required. If the robot wants to learn to operate a bulldozer, all he needs is to read a short instruction manual or by watching other people operate the machine. Also, there are no programmers spending 5-10 years writing the internal codes for each vehicle. The robot has universal intelligence and can operate any machine, even machines that are unknown.

Knowledge can also be shared among different vehicles. For example, knowledge of driving a car is used to drive a forklift. Thus, the robot doesn't need to relearn how to turn the steering wheel or avoid obstacles in environment. Common sense will allow the robot to share knowledge for similar vehicles. For example, a motorcycle, a truck, and a car are vehicles driven on the streets. They follow very similar traffic rules and operate in similar manners. For each vehicle, the robot will create an optimal computer program in its mind to operate it. If the robot had to drive a truck, optimal rules and objectives are activated in the robot's mind. If the robot had to drive a car, optimal rules and objectives are activated in the robot's mind, etc, etc.

By the way, my robot doesn't use: planning programs/heuristic searches (used by MIT and Stanford University), Bayesian's probability theories for decision making, Bayesian's equation for induction and deduction, semantic networks for natural language understanding, predicate calculus, common sense systems, first-order logic, rule-based systems, genetic programming, or MACHINE LEARNING. All knowledge in the robot's brain comes from knowledge taught in school or from books.