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Supervised learning has its place, and it provides great utility for many di erent pattern-matching problems. There are times, however, where you want your system to learn on its own. Not all problems are easy to break down into supervised learning sets. Fortunately for us, almost all real phenomena occurs as a continuous stream of events, each of which is related, by physics and the ow of time, to the previous one. Our sensory input is continuous, with strong internal structures and a de nite sense of cause and e ect. Neural networks can make use of this coherence to create statistical groups of sensory patterns. These groups divide the network s experience into signi cant bundles using unsupervised learning. Some unsupervised networks, called self-organizing maps (SOM), learn by comparing the current sensory input against the history of all inputs as embodied by the patterns of their neurons. Where supervised learning is given the answers, unsupervised learning discovers the answers in the very textures of its experience. The internal details of the neurons are still the same: computational neurons containing templates that are matched to the input pattern, to create an output activation. But in training, the activated neurons are not compared to the desired output but instead to their neighbors. In a neighborhood group, the best matching neuron adjusts to better match the input, while more distant neurons move away from that pattern. Once trained, each neuron in the map represents a point in state space. State space is like a map of all possible inputs, in all possible combinations. In reality, only a small set of input combinations are going to appear on the input of the network. The network adjusts to represent the inputs that appear and pretty much ignores areas of state space that are not relevant. Mapping the self-organizing map to robot control is a bit tricky. In theory, the system could learn what actions are appropriate to which inputs through trial and error, and with some form of reward and punishment system. There is a system of learning called reinforcement learning that does just this. In practice, the SOM may be hooked up to a more traditional multilayer perceptron. The activation levels of the neurons in the map become inputs to the MLP, which is trained so that the correct control signals are generated. In this case the SOM is a pre-processor for the MLP. It can take a large, poorly de ned, and imprecise set of inputs and simplify them.
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CHAPTER 18 Advanced Control
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The robots you see in the movies, the C3POs and the androids, are built in our own image. A single machine with exible manipulators (arms, hands, and ngers) capable of traversing most forms of terrain (legs, feet) and with a single, complex control system (brain). Industrial robots are almost the direct opposite of this. They are specialpurpose machines specialized for a single task, they tend to be xed in position, and their control system is little more than a script reader playing back preset instructions. There is a third approach to robots, almost alien in nature. Their inspiration is in the complex communities and behaviors of ants and bees. A single ant is not very complex. Building an ant brain is a far more approachable task than building a human brain. Ants in groups have more complex behavior than an ant by itself. Much like a brain has behavior beyond that which could be anticipated by looking at a single neuron. This thinking brings us to swarm robotics. Each robot is a small, inexpensive, and simply programmed unit. It has the ability to communicate with neighboring robots and it is deployed with a large number of its siblings. Any small subset of these robots can be destroyed or lost and the swarm can still ful ll its job. The robots are inexpensive so there can be a bunch of them, and since there are a bunch of them any one of them is expendable. This con guration of robots, the swarm, lends itself to exploration. Exploration of space leaps to mind, but there are also important exploration tasks on Earth, such as swarming over a disaster area looking for survivors. Swarms will also be used in wartime, to scout out the urban landscape and report on conditions. Swarms of ying robots, another topic that has generated a lot of interest lately, are especially useful for scouting. Related to swarm robotics is modular robotics. In this case, the swarm comes together, the individual robots attach to each other physically. This makes them into a larger robot for the purpose of completing a task that is not possible for a single, smaller robot.
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