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Topics: Introduction to Robotics CS 491/691(X)

This lecture covers hybrid control, supervised learning, neural networks, learning from demonstration, classical conditioning, genetic algorithms, classifier systems, evolving structure and control, and fuzzy control in robotics.

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Topics: Introduction to Robotics CS 491/691(X)

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  1. Topics: Introduction to RoboticsCS 491/691(X) Lecture 13 Instructor: Monica Nicolescu

  2. Review • Hybrid control • Selection, Advising, Adaptation, Postponing • AuRA, Atlantis, Planner-Reactor, PRS, many others • Adaptive behavior • Adaptation vs. learning • Challenges • Reinforcement learning, examples (learning to walk, learning to push) CS 491/691(X) - Lecture 13

  3. 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 CS 491/691(X) - Lecture 13

  4. Neural Networks • One of the most used supervised learning methods • Used for approximating real-valued and vector-valued target functions • Inspired from biology: learning systems are built from complex networks of interconnecting neurons • 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 CS 491/691(X) - Lecture 13

  5. Training Neural Networks • Hebbian learning • Increases synaptic strength along neural pathways associated with a stimulus and a correct response • Perceptron learning • Delta Rule: for networks without hidden layers • Back-propagation: for multi-layer networks CS 491/691(X) - Lecture 13

  6. Perceptron Learning Repeat • Present an example from a set of positive and negative learning experiences • Verify the output of the network as to whether it is correct or incorrect • If it is incorrect, supply the correct output at the output unit • Adjust the synaptic weights of the perceptrons in a manner that reduces the error between the observed output and the correct output Until satisfactory performance (convergence or stopping condition is met) CS 491/691(X) - Lecture 13

  7. ALVINN • ALVINN (Autonomous Land Vehicle in a Neural Network) • Dean Pomerleau (1991) • Pittsburg to San Diego: 98.2% autonomous CS 491/691(X) - Lecture 13

  8. Learning from Demonstration & RL • S. Schaal (’97) • Pole balancing, pendulum-swing-up CS 491/691(X) - Lecture 13

  9. Learning from Demonstration Inspiration: • Human-like teaching by demonstration Demonstration Robot performance CS 491/691(X) - Lecture 13

  10. Learning from Robot Teachers • Transfer of task knowledge from humans to robots Human demonstration Robot performance CS 491/691(X) - Lecture 13

  11. Classical Conditioning • Pavlov 1927 • Assumes that unconditioned stimuli (e.g. food) automatically generate an unconditioned response (e.g., salivation) • Conditioned stimulus (e.g., ringing a bell) can, over time, become associated with the unconditioned response CS 491/691(X) - Lecture 13

  12. Darvin VII • G. Edelman et. Al. • Darvin VII Sensors • CCD Camera • Gripper that senses conductivity • IR sensors • Darvin VII Actuators • PTZ camera • Wheels • Gripper • Low reflectivity walls, floor • Two types of stimulus blocks • 6cm metallic cubes • Blobs: low conductivity (“bad taste”) • Stripes: high conductivity (“good taste”) CS 491/691(X) - Lecture 13

  13. Darvin’s Perceptual Categorization • Instead of hard-wiring stimulus-response rules, develop these associations over time Early training After the 10th stimulus CS 491/691(X) - Lecture 13

  14. Genetic Algorithms • Inspired from evolutionary biology • Individuals in a populations have a particular fitness with respect to a task • Individuals with the highest fitness are kept as survivors • Individuals with poor performance are discarded: the process of natural selection • Evolutionary process: search through the space of solutions to find the one with the highest fitness CS 491/691(X) - Lecture 13

  15. Genetic Operators • Knowledge is encoded as bit strings: chromozome • Each bit represents a “gene” • Biologically inspired operators are applied to yield better generations CS 491/691(X) - Lecture 13

  16. Classifier Systems • ALECSYS system • Learns new behaviors and coordination • Genetic operators act upon a set of rules encoded by bit strings • Demonstrated tasks: • Phototaxis • Coordination of approaching, chasing and escaping behaviors by combination, suppression and sequencing CS 491/691(X) - Lecture 13

  17. Evolving Structure and Control • Karl Sims 1994 • Evolved morphology and control for virtual creatures performing swimming, walking, jumping, and following • Genotypes encoded as directed graphs are used to produce 3D kinematic structures • Genotype encode points of attachment • Sensors used: contact, joint angle and photosensors CS 491/691(X) - Lecture 13

  18. Evolving Structure and Control • Jordan Pollak • Real structures CS 491/691(X) - Lecture 13

  19. Fuzzy Control • Fuzzy control produces actions using a set of fuzzy rules based on fuzzy logic • In fuzzy logic, variables take values based on how much they belong to a particular fuzzy set: • Fast, slow, far, near – not crisp values!! • A fuzzy logic control system consists of: • Fuzzifier: maps sensor readings to fuzzy input sets • Fuzzy rule base: collection of IF-THEN rules • Fuzzy inference: maps fuzzy sets to other fuzzy sets according to the rulebase • Defuzzifier: maps fuzzy outputs to crisp actuator commands CS 491/691(X) - Lecture 13

  20. Examples of Fuzzy Control • Flakey the robot: • Behaviors are encoded as collections of fuzzy rules IF obstacle-close-in-front AND NOT obstacle-close-on-left THEN turn sharp-left • Each behavior may be active to a varying degree • Behavior responses are blended smoothly • Multiple goals can be pursued • Systems for learning fuzzy rules have also been developed CS 491/691(X) - Lecture 13

  21. Where Next? CS 491/691(X) - Lecture 13

  22. Fringe Robotics: Beyond Behavior Questions for the future • Human-like intelligence • Robot consciousness • Complete autonomy of complex thought and action • Emotions and imagination in artificial systems • Nanorobotics • Successor to human beings CS 491/691(X) - Lecture 13

  23. A Robot Mind • The goal of AI is to build artificial minds • What is the mind? • “The mind is what the brain does.” (M. Minsky) • The mind includes • thinking • feeling CS 491/691(X) - Lecture 13

  24. Computational Thought • What does it mean for a machine to think? • Bellman • Thought is not well defined, so we cannot ascribe/judge it • Computers can perform processes representative of human thought: decision making/learning • Albus • For robots to understand humans, they must be indistinguishable from humans in bodily appearance, physical and mental development • Brooks: • Thought and consciousness need not be programmed in: they will emerge CS 491/691(X) - Lecture 13

  25. The Turing Test • Developed by the mathematician Alan Turing Original version of Turing Test: • Two people (a man and a woman) are put in separate closed rooms. A third person can interact with each of the two through writing (no voices). • Can the 3rd person tell the difference between the man and the woman? CS 491/691(X) - Lecture 13

  26. The Turing Test AI version of the Turing Test: • A person sits in front of two terminals: at one end is a human at the other end is a computer. The questioner is free to ask any questions to the respondents at the other end of the terminals • If the questioner cannot tell the difference between the computer and the human subject, the computer has passed the Turing Test! CS 491/691(X) - Lecture 13

  27. The Turing Test • The Turing Test contest is performed annually, and it carries a $100,000 award for anybody who passes it • No computer so far has truly passed the Turing Test • Is this a good test of intelligence? • Thought is defined based on human fallibility rather than on machine consciousness • Many researchers oppose to using this test as a proof of intelligence CS 491/691(X) - Lecture 13

  28. Penrose’s Critique • Roger Penrose (Emperor’s new Mind, Shadows of the Mind), a British physicist, is a famous critic of AI • Intelligence is a consequence of neural activity and interactions in the brain • Computers can only simulate this activity, but this is not sufficient for true intelligence • Intelligence requires understanding, and understanding requires awareness, an aspect of consciousness • Many refuting arguments have been given CS 491/691(X) - Lecture 13

  29. “They're Made Out Of Meat“ Terry Bisson "They're made out of meat.“ "Meat?“ "Meat. They're made out of meat.“ "Meat?“ "There's no doubt about it. We picked several from different parts of the planet, took them aboard our recon vessels, probed them all the way through. They're completely meat.“ "That's impossible. What about the radio signals? The messages to the stars.“ "They use the radio waves to talk, but the signals don't come from them. The signals come from machines.“ "So who made the machines? That's who we want to contact." CS 491/691(X) - Lecture 13

  30. “They're Made Out Of Meat“ Terry Bisson "They made the machines. That's what I'm trying to tell you. Meat made the machines.“ That's ridiculous. How can meat make a machine? You're asking me to believe in sentient meat.“ "I'm not asking you, I'm telling you. These creatures are the only sentient race in the sector and they're made out of meat.“ "Maybe they're like the Orfolei. You know, a carbon-based intelligence that goes through a meat stage.“ "Nope. They're born meat and they die meat. We studied them for several of their life spans, which didn't take too long. Do you have any idea what’s the life span of meat?“ "Spare me. Okay, maybe they're only part meat. You know, like the Weddilei. A meat head with an electron plasma brain inside." CS 491/691(X) - Lecture 13

  31. “They're Made Out Of Meat“ Terry Bisson "Nope. We thought of that, since they do have meat heads like the Weddilei. But I told you, we probed them. They're meat all the way through.“ "No brain?“ "Oh, there is a brain all right. It's just that the brain is made out of meat!“ "So... what does the thinking?" "You're not understanding, are you? The brain does the thinking. The meat.“ "Thinking meat! You're asking me to believe in thinking meat!“ "Yes, thinking meat! Conscious meat! Loving meat. Dreaming meat. The meat is the whole deal! Are you getting the picture?" CS 491/691(X) - Lecture 13

  32. Conclusion Lots of remaining interesting problems to explore! Get involved! CS 491/691(X) - Lecture 13

  33. Readings • Lecture notes CS 491/691(X) - Lecture 13

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