Behavior-Based Robo

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RoboticaLezione 14

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Lecture Outline Neural networks Classical conditioning AHC with NNs Genetic Algorithms Classifier Systems Fuzzy learning Case-based learning Memory-based learning Explanation-based learning

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Q Learning Algorithm Q(x,a)  Q(x,a) + b*(r + *E(y) - Q(x,a))‏ x is state, a is action b is learning rate r is reward  is discount factor (0,1)‏ E(y) is the utility of the state y, computed as E(y) = max(Q(y,a)) for all actions a Guaranteed to converge to optimal, given infinite trials

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Supervised Learning Supervised learning requires the user to give the exact solution to the robot in the form of the error direction and magnitude. The user must know the exact desired behavior for each situation. Supervised learning involves training, which can be very slow; the user must supervise the system with numerous examples.

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Neural Networks NNs are the most widely used example of supervised learning There are numerous types of NNs (feed-forward, recurrent) and NN learning algorithms Hebbian learning: increase synaptic strength along pathways associated with stimulus and correct response Perceptron learning: delta rule or back-propagation

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Perceptron LearningAlgorithm:

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NNs are RL In all NNs, the goal is to minimize the error between the network output and the desired output This is achieved by adjusting the weights on the network connections Note: NNs are a form of reinforcement learning NNs perform supervised RL with immediate error feedback

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Classical Conditioning Classical conditioning comes from psychology (Pavlov 1927)‏ Assumes that unconditioned stimuli (e.g., food) cause unconditioned responses (e.g., salivation); US => UR A conditioned stimulus is, over time, associated with an unconditioned response (CS=>UR)‏ E.g., CS (bell ringing) => UR (salivation)‏

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Classical Conditioning Instead of encoding SR rules, conditioning can be used to from the associations automatically Can be encoded in NNs

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Connectionist AHC Robuter learned a set of gain multipliers for exploration (Gachet et al)‏

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Associative Learning Learning new behaviors by associating sensors and actions into rules E.g.,: 6-legged walking (Edinburgh U.)‏ Whisker sensors first, IR and light later 3 actions: left,right, ahead User provided feedback (shaping)‏ Learned avoidance, pushing, wall following, light seeking

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Two-Layer Perceptron Arch.

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More NN Examples Some domains and tasks lend themselves very well to supervised NN learning The best example is robot motion planning for articulation/manipulation The answer to any given situation is well known, and can be trained E.g., NNs are widely used for learning inverse kinematics

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Evolutionary Methods Genetic/evolutionary approaches are based on the evolutionary search metaphor The states/situations and actions/behaviors are represented as "genes” Different combinations are tried by various "individuals" in "populations" Individuals with the highest "fitness" perform the best, are kept as survivors

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Evolutionary Methods The others are discarded; this is the selection process. The survivors' "genes" are mutated, crossed-over, and new individuals are so formed, which are then tested and scored. In effect, the evolutionary process is searching through the space of solutions to find the one with the highest fitness.

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Summary of Evolution Evolutionary methods solve search and optimization problems using a fitness function operators They represent the solution as a genetic encoding (string)‏ They select ‘best’ individuals for reproduction and apply Cross over, mutation They operate on populations

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Genetic Algorithms

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Levels of Application Genetic methods can be applied at different levels: 1) for tuning parameters (such as gains in a control system)‏ 2) for developing controllers (policies) for individual robots 3) for developing group strategies for multi-robot systems (by testing groups as populations)

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GAs v. GPs When applied to strings of genes, the approaches are classified as genetic algorithms (GA)‏ When applied to pieces of executable programs, they approaches are classified as genetic programming (GP)‏ GP operates at a higher level of abstraction than GA

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Classifier Systems Use GAs to learn rule sets ALECSYS - Autono-mouse Learn behaviors and coordination

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Evolving Structure & Behavior Evolving morphology & behavior (Sims 1994) using a physics-based model Fitness in simulation through competition (e.g., walking and swimming)‏

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Co-evolving Brains and BodiesExtension to the physical world (Pollack)

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Fuzzy Logic Fuzzy control produces actions using a set of fuzzy rules based on fuzzy logic This involves: fuzzifying: mapping sensor readings into a set of fuzzy inputs fuzzy rule base: a set of IF-THEN rules fuzzy inference: maps fuzzy sets onto other fuzzy sets using membership fncts. defuzzifying: mapping a set of fuzzy outputs onto a set of crisp output commands

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Fuzzy Control Fuzzy logic allows for specifying behaviors as fuzzy rules Such behaviors can be smoothly blended together (e.g., Flakey, DAMN)‏ Fuzzy rules can be learned

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Memory-Based Learning Memory-based learning (MBL) involves storing the exact parameters of a situation/action. This type of learning is important for systems with a great deal of parameters, where blind search through parameter space would take prohibitively long. MBL takes a lot of space for data storage.

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Memory-Based Learning This is a trade-off against computation time; MBL assumes that it is best to use the space to remember than time to compute. Content-addressable memory is used to approximate output for a new input This is a form of interpolation The approach uses regression (basic intuition is weighted averaging)‏

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Last Updated: 8th March 2018

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