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GTA 5
Grand Theft Auto V is an open world, action-adventure video game developed by Rockstar North and published by Rockstar Games. It was released on 17 September 2013 for the PlayStation 3 and Xbox 360, on 18 November 2014 for the PlayStation 4 and Xbox One, and on 14 April 2015 for Microsoft Windows. The game is the first main entry in the Grand Theft Auto series since 2008's Grand Theft Auto IV. Set within the fictional state of San Andreas (based on Southern California), the single-player story follows three criminals and their efforts to commit heists while under pressure from a government agency. The open world design lets players freely roam San Andreas, which includes open countryside and the fictional city of Los Santos (based on Los Angeles).
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The game is played from either a third-person or first-person view[c] and its world is navigated on foot or by vehicle. Players control the three lead protagonists throughout the single-player mode, switching between them both during and outside of missions. The story is centred on the heist sequences, and many of the missions involve shooting and driving gameplay. Players who commit crimes may incite a response from law enforcement agencies, measured by a "wanted" system that governs the aggression of their response. Grand Theft Auto Online, the online multiplayer mode, lets up to 30 players explore the open world and engage in cooperative or competitive game matches.
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How Will Robots Evolve? watch with "ME"
Evolutionary robotics (ER) is a methodology that uses evolutionary computation to develop controllers for autonomous robots. Algorithms in ER frequently operate on populations of candidate controllers, initially selected from some distribution. This population is then repeatedly modified according to a fitness function. In the case of genetic algorithms (or "GAs"), a common method in evolutionary computation, the population of candidate controllers is repeatedly grown according to crossover, mutation and other GA operators and then culled according to the fitness function. The candidate controllers used in ER applications may be drawn from some subset of the set of artificial neural networks, although some applications (including SAMUEL, developed at the Naval Center for Applied Research in Artificial Intelligence) use collections of "IF THEN ELSE" rules as the constituent parts of an individual controller. It is theoretically possible to use any set of symbolic formulations of a control law (sometimes called a policy in the machine learning community) as the space of possible candidate controllers. Artificial neural networks can also be used for robot learning outside of the context of evolutionary robotics. In particular, other forms of reinforcement learning can be used for learning robot controllers.
Developmental robotics is related to, but differs from, evolutionary robotics. ER uses populations of robots that evolve over time, whereas DevRob is interested in how the organization of a single robot's control system develops through experience, over time.
History
The foundation of ER was laid with work at the national research council in Rome in the 90s, but the initial idea of encoding a robot control system into a genome and have artificial evolution improve on it dates back to the late 80s.
In 1992 and 1993 three research groups, one surrounding Floreano and Mondada at the EPFL in Lausanne and a second involving Cliff, Harvey, and Husbands from COGS at the University of Sussex and a third from the University of Southern California involved M. Anthony Lewis and Andrew H Fagg reported promising results from experiments on artificial evolution of autonomous robots. The success of this early research triggered a wave of activity in labs around the world trying to harness the potential of the approach.
Lately, the difficulty in "scaling up" the complexity of the robot tasks has shifted attention somewhat towards the theoretical end of the field rather than the engineering end.
Robot See, Robot Do: How Robots Can Learn New Tasks by Observing
The process could be sped up significantly if robots were able to learn how to do a new job by watching others do it first. That’s the idea behind a project underway at the University of Maryland, where researchers are teaching robots to be attentive students.
“We call it a ‘robot training academy,’” says Yezhou Yang, a graduate student in the Autonomy, Robotics and Cognition Lab at the University of Maryland. “We ask an expert to show the robot a task, and let the robot figure out most parts of sequences of things it needs to do, and then fine-tune things to make it work.”
At a recent conference in St. Louis, the researchers demonstrated a cocktail-making robot that uses the approaches they’re working on. The robot—a two-armed industrial machine made by a Boston-based company called Rethink Robotics, watched a person mix a drink by pouring liquid from several bottles into a jug, and would then copy those actions, grasping bottles in the correct order before pouring the right quantities into the jug. Yang carried out the work with Yiannis Aloimonos and Cornelia Fermuller, two professors of computer science at the University of Maryland.
The approach involves training a computer system to associate specific robot actions with video footage showing people performing various tasks. A recent paper from the group, for example, shows that a robot can learn how to pick up different objects using two different systems by watching thousands of instructional YouTube videos. One system learns to recognize different objects; another identifies different types of grasp.
Watching thousands of YouTube videos may sound time-consuming, but the learning approach is more efficient than programming a robot to handle countless different items, and it can enable a robot to deal with a new object. The learning systems used for the grasping work involved advanced artificial neural networks, which have seen rapid progress in recent years and are now being used in many areas of robotics.
The project reflects two trends in robotics; one is finding new approaches to learning, and another is robots working in close proximity with people. Like other groups, the Maryland researchers wants to connect actions to language to improve robots’ ability to parse spoken or written instructions (see “Robots Learn to Make Pancakes from WikiHow Articles”).
Other academics are also investigating ways of making robots that can learn. A group led by Pieter Abbeel at the University of California, Berkeley, is exploring ways for robots to learn through experimentation. Julie Shah, a professor at MIT, is developing ways for robots to learn not only how to perform a task, but also how to collaborate more effectively with human coworkers (see “Innovators Under 35: Julie Shah”).
Robots Learn to Make Pancakes
Researchers at a European project are teaching robots to use written text to learn how to perform tasks.
If you’ve ever needed to know how to tie a bowtie or fix a strawberry daiquiri, you likely ended up on a website like WikiHow for step-by-step instructions. Surprisingly, some robots are now doing the same.
A robot called PR2 in Germany is learning to prepare pancakes and pizzas by carefully reading through WikiHow’s written directions. It’s part of a European project called RoboHow, which is exploring ways of teaching robots to understand language. This could make it easier for people to communicate instructions to robots and provide a way for machines to figure out how to perform unfamiliar tasks. Instead of programming a robot to perform precise movements, the goal is for a person to simply tell a robot what to do.
Teaching robots how to turn high-level descriptions into specific actions is an important but challenging task. It is straightforward for humans because we have an understanding of all sorts of basic tasks, collected over a lifetime. A human does not need to be told the specific grasp needed to remove the top from a jar of tomato sauce, for instance, or that flipping a pancake involves using a spatula or some other kitchen utensil.
So the researchers behind the RoboHow project want to teach robots the general knowledge required to turn high-level instructions into specific actions. They have so far been able to convert a few WikiHow instructions into useful behavior, both in simulations and in real robots.
Achieving more could prove very useful as robots become more commonplace and need to work more closely with people. “If you have a robot in a factory, you want to say ‘Take the screw and put it into the nut and fasten the nut,’” says Michael Beetz, head of the Artificial Intelligence Institute at the University of Bremen in northern Germany, where the RoboHow project is based. “You want the robot to generate the parameters automatically out of the semantic description of objects.”
In one set of experiments, the researchers are teaching PR2 robots to perform simple lab tasks, such as handling chemicals.
Once a robot has learned how a particular set of instructions relates to a task, its knowledge is added to an online database called Open Ease, so that other robots can access that understanding. These instructions are encoded in machine-readable language similar to the one used in the Semantic Web project.
A robot called PR2 in Germany is learning to prepare pancakes and pizzas by carefully reading through WikiHow’s written directions. It’s part of a European project called RoboHow, which is exploring ways of teaching robots to understand language. This could make it easier for people to communicate instructions to robots and provide a way for machines to figure out how to perform unfamiliar tasks. Instead of programming a robot to perform precise movements, the goal is for a person to simply tell a robot what to do.
Teaching robots how to turn high-level descriptions into specific actions is an important but challenging task. It is straightforward for humans because we have an understanding of all sorts of basic tasks, collected over a lifetime. A human does not need to be told the specific grasp needed to remove the top from a jar of tomato sauce, for instance, or that flipping a pancake involves using a spatula or some other kitchen utensil.
So the researchers behind the RoboHow project want to teach robots the general knowledge required to turn high-level instructions into specific actions. They have so far been able to convert a few WikiHow instructions into useful behavior, both in simulations and in real robots.
Achieving more could prove very useful as robots become more commonplace and need to work more closely with people. “If you have a robot in a factory, you want to say ‘Take the screw and put it into the nut and fasten the nut,’” says Michael Beetz, head of the Artificial Intelligence Institute at the University of Bremen in northern Germany, where the RoboHow project is based. “You want the robot to generate the parameters automatically out of the semantic description of objects.”
In one set of experiments, the researchers are teaching PR2 robots to perform simple lab tasks, such as handling chemicals.
Once a robot has learned how a particular set of instructions relates to a task, its knowledge is added to an online database called Open Ease, so that other robots can access that understanding. These instructions are encoded in machine-readable language similar to the one used in the Semantic Web project.
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The researchers are using other techniques to help robots learn to perform basic tasks. This includes watching videos of humans performing those tasks and studying virtual-reality data when humans have performed tasks wearing gloves that allow their actions to be tracked.
Even simple manipulation remains a challenge for robots, although many researchers, including those at Amazon, are pushing to develop better robot grasping (see “Help Wanted: Robot to Fulfill Amazon Orders”). Natural language processing is also very challenging, but progress is being made here, too (see “Teaching Machines to Understand Us”).
Siddhartha Srinivasa, a professor at the Robotics Institute at Carnegie Mellon University, says connecting language with action is hugely important but also very difficult. “I have a four-year-old and often face disaster when I try to instruct him to assemble a toy,” Srinivasa says. “Succeeding in this domain will require a tight integration of natural language, grounding the understanding via perception, and planning complex actions via manipulation algorithms.”
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