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Advanced Topics in Robotics: CS493/790 (X) Lecture 1

This lecture covers fundamental aspects of robotics, including robot control architectures, advanced robotics techniques, biologically inspired robotics, robot learning, multiple robot systems, human-robot interaction, navigation and mapping, and hands-on experience.

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Advanced Topics in Robotics: CS493/790 (X) Lecture 1

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  1. Advanced Topics in Robotics CS493/790 (X) Lecture 1 Instructor: Monica Nicolescu

  2. General Information • Instructor: Dr. Monica Nicolescu • E-mail: monica@cs.unr.edu • Office hours: Tuesday, Thursday; 11:00am-noon • Room: SEM 239 • Class webpage: • http://www.cs.unr.edu/~monica/Courses/CS493-790/ • Lectures • Tuesday: 9:30-10:45am SEM 344 • Laboratory • Thursday: 9:30-10:45am SEM 246 CS 493/790(X) - Lecture 1

  3. What will we Learn? • Cover fundamental aspects of robotics • What is a robot? • Robot control architectures • Advanced robotics techniques • Biologically inspired robotics • Robot learning: reinforcement, imitation, demonstration, genetic algorithms • Multiple robot systems: coordination and cooperation • Human-robot interaction • Navigation and mapping • Hands-on experience CS 493/790(X) - Lecture 1

  4. Readings and Presentations • Two papers (on average) discussed at each lecture • Each paper is presented by a student • Presentation guidelines • At most 30 minutes • Briefly summarize the paper • Discuss the paper, its strengths, weaknesses, any points needing clarification • Addressing any questions the other students may have CS 493/790(X) - Lecture 1

  5. Readings and Paper Reports • For each paper, all students must submit, at the beginning of the class a brief report of the paper • Report format (typed) • Student's name • Title and authors of the paper • A short paragraph summarizing the contributions of the paper • A critique of the paper that addresses the strengths and weaknesses of the paper CS 493/790(X) - Lecture 1

  6. Project/Lab Testbeds • The Player-Stage-Gazebo simulator (playerstage.sourceforge.net) • Player is a general purpose language-indepedent network server for robot control • Stage is a Player-compatible high-fidelity indoor multi-robot simulation testbed • Gazebo is a Player-compatible high-fidelity 3D outdoor simulation testbed with dynamics • Player/Stage/Gazebo allows for direct porting to Player-compatible physical robots. CS 493/790(X) - Lecture 1

  7. Project/Lab Testbeds • One Player-compatible ActivMedia Pioneer 3 DX • sonar sensors • Laser • PTZ camera • Onboard computer • One Player-compatible ActivMedia Pioneer 1 AT robot • 7 sonar sensors and requires the use of a laptop (not provided) • 16 LEGO robot kits • Handy Board microcontroller • Programming in Interactive C CS 493/790(X) - Lecture 1

  8. Project • Individual project on topics covered in class • Project topics: an implementation of either: • a single robot system (involving complex behavior and demonstrated on a physical robot) or • a multi-robot system (involving cooperation/ communication/ coordination between robots and demonstrated in simulation) CS 493/790(X) - Lecture 1

  9. Project Reports • Should include the following: • Title, author • Abstract • Introduction and motivation • Problem definition: project goals, assumptions, constraints, and evaluation criteria • Details of proposed approach • Results and objective experimental evaluation • Review of relevant literature • Discussion (strengths and weaknesses) and conclusion • References • Appendix (relevant code or algorithms) CS 493/790(X) - Lecture 1

  10. Class Policy • Grading • Paper reports: 15% • Paper presentations: 20% • Participation in class discussions: 15% • Lab assignments: 20% • Final project: 30% • Late submissions • No late submissions will be accepted • Attendance • Full participation in class discussions CS 493/790(X) - Lecture 1

  11. Important Dates/Milestones • February 23 • Project topic proposal and presentation • One page that outlines the specific goals, contribution, implementation platform and the proposed approach • April 6 • Project status presentations • 5 minute in-class presentation • One-two pages that describe the current status of the project, what has been done, what is still to be done CS 493/790(X) - Lecture 1

  12. Important Dates/Milestones • May 12 • Project final presentations  • Project final demonstrations • Project final reports due CS 493/790(X) - Lecture 1

  13. Optional Textbooks • Basic topics • The Robotics Primer, 2001. Author: Maja Mataric' • Available in draft form at the bookstore • Advanced topics • Behavior-Based Robotics, 2001.Author: Ron Arkin • Available at the library • Lego Robots • Robotic Explorations: An Introduction to Engineering Through Design, 2001. Author: Fred G. Martin CS 493/790(X) - Lecture 1

  14. Key Concepts • Situatedness • Agents are strongly affected by the environment and deal with its immediate demands (not its abstract models) directly • Embodiment • Agents have bodies, are strongly constrained by those bodies, and experience the world through those bodies, which have a dynamic with the environment CS 493/790(X) - Lecture 1

  15. Key Concepts (cont.) • Situated intelligence • is an observed property, not necessarily internal to the agent or to a reasoning engine; instead it results from the dynamics of interaction of the agent and environment • and behavior are the result of many interactions within the system and w/ the environment, no central source or attribution is possible CS 493/790(X) - Lecture 1

  16. The term “robot” • Karel Capek’s 1921 play RUR (Rossum’s Universal Robots) • It is (most likely) a combination of “rabota” (obligatory work) and “robotnik” (serf) • Most real-world robots today do perform such “obligatory work” in highly controlled environments • Factory automation (car assembly) • But that is not what robotics research about; the trends and the future look much more interesting CS 493/790(X) - Lecture 1

  17. What is in a Robot? • Sensors • Effectors and actuators • Used for locomotion and manipulation • Controllers for the above systems • Coordinating information from sensors with commands for the robot’s actuators • Robot = an autonomoussystem which exists in the physical world, can senseits environment and canacton it to achieve some goals CS 493/790(X) - Lecture 1

  18. Challenges • Perception • Limited, noisy sensors • Actuation • Limited capabilities of robot effectors • Thinking • Time consuming in large state spaces • Environments • Dynamic, impose fast reaction times CS 493/790(X) - Lecture 1

  19. Uncertainty • Uncertainty is a key property of existence in the physical world • Physical sensors provide limited, noisy, and inaccurate information • Physical effectors produce limited, noisy, and inaccurate action • The uncertainty of physical sensors and effectors is not well characterized, so robots have no available a priori models CS 493/790(X) - Lecture 1

  20. Uncertainty (cont.) • A robot cannot accurately know the answers to the following: • Where am I? • Where are my body parts, are they working, what are they doing? • What did I just do? • What will happen if I do X? • Who/what are you, where are you, what are you doing, etc.?... CS 493/790(X) - Lecture 1

  21. Classical activity decomposition • Locomotion (moving around, going places) • factory delivery, Mars Pathfinder, lawnmowers, vacuum cleaners... • Manipulation (handling objects) • factory automation, automated surgery... • This divides robotics into two basic areas • mobile robotics • manipulator robotics • … but these are merging in domains like robot pets, robot soccer, and humanoids CS 493/790(X) - Lecture 1

  22. Robots: Alternative Terms • UAV • unmanned aerial vehicle • UGV (rover) • unmanned ground vehicle • UUV • unmanned undersea vehicle CS 493/790(X) - Lecture 1

  23. An assortment of robots… CS 493/790(X) - Lecture 1

  24. Anthropomorphic Robots CS 493/790(X) - Lecture 1

  25. Animal-like Robots CS 493/790(X) - Lecture 1

  26. Humanoid Robots QRIO Asimo (Honda) CS 493/790(X) - Lecture 1 DB (ATR) Robonaut (NASA) Sony Dream Robot

  27. A Brief History of Robotics • Robotics grew out of the fields of control theory, cyberneticsandAI • Robotics, in the modern sense, can be considered to have started around the time of cybernetics (1940s) • Early AI had a strong impact on how it evolved (1950s-1970s), emphasizing reasoning and abstraction, removal from direct situatedness and embodiment • In the 1980s a new set of methods was introduced and robots were put back into the physical world CS 493/790(X) - Lecture 1

  28. W. Grey Walter’s Tortoise • Machina Speculatrix” (1953) • 1 photocell, 1 bump sensor, 1 motor, 3 wheels, 1 battery • Behaviors: • seek light • head toward moderate light • back from bright light • turn and push • recharge battery • Uses reactive control, with behavior prioritization CS 493/790(X) - Lecture 1

  29. Braitenberg Vehicles • Valentino Braitenberg (1980) • Thought experiments • Use direct coupling between sensors and motors • Simple robots (“vehicles”) produce complex behaviors that appear very animal, life-like • Excitatory connection • The stronger the sensory input, the stronger the motor output • Light sensor  wheel: photophilic robot (loves the light) • Inhibitory connection • The stronger the sensory input, the weaker the motor output • Light sensor  wheel: photophobic robot (afraid of the light) CS 493/790(X) - Lecture 1

  30. Example Vehicles • Wide range of vehicles can be designed, by changing the connections and their strength • Vehicle 1: • One motor, one sensor • Vehicle 2: • Two motors, two sensors • Excitatory connections • Vehicle 3: • Two motors, two sensors • Inhibitory connections Vehicle 1 Being “ALIVE” “FEAR” and “AGGRESSION” Vehicle 2 “LOVE” CS 493/790(X) - Lecture 1

  31. Artificial Intelligence • Officially born in 1956 at Dartmouth University • Marvin Minsky, John McCarthy, Herbert Simon • Intelligence in machines • Internal models of the world • Search through possible solutions • Plan to solve problems • Symbolic representation of information • Hierarchical system organization • Sequential program execution CS 493/790(X) - Lecture 1

  32. AI and Robotics • AI influence to robotics: • Knowledge and knowledge representation are central to intelligence • Perception and action are more central to robotics • New solutions developed: behavior-based systems • “Planning is just a way of avoiding figuring out what to do next” (Rodney Brooks, 1987) • Distributed AI (DAI) • Society of Mind (Marvin Minsky, 1986): simple, multiple agents can generate highly complex intelligence • First robots were mostly influenced by AI (deliberative) CS 493/790(X) - Lecture 1

  33. Background Readings • F. Martin: Sections 1.1, 1.2.3 • M. Matarić: Chapters 1, 3 CS 493/790(X) - Lecture 1

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