1. Intelligent Robotics: Introduction and Course Overview Intelligent Robotics 06-13520 Intelligent Robotics (Extended) 06-15267 Jeremy Wyatt School of Computer Science University of Birmingham, 2005

2. Plan Intellectual aims of the module Task description Introduction to hardware and software Using robots to understand intelligence

3. Module aims  Give an appreciation of the issues that arise when designing complete, physically embodied autonomous agents.  Introduce some of the most popular methods for controlling autonomous mobile robots.  Give hands on experience of engineering design.  Encourage independent thought on possible cognitive architectures for autonomous agents.

4. What you will be able to do Design, build and program simple autonomous robots. Implement standard signal processing and control algorithms. Describe and analyse robot processes using appropriate methods. Write a detailed report on a robot project. Carry out and write up investigations using appropriate experimental methods.

5. Basic Hardware IR sensors (long, medium, short) Whiskers Microswitches DC motors Servo motors Odometers Sonar

6. Additional Hardware PC104 board + Handyboard PC104 (Linux) USB HB Sensors Motors Camera USB

7. Coding on the PC104 Vision routines can be written easily using extensive libraries from Intel Multiple processes: threads are wrapped Download and Run Managers Support on Handyboard for –pulse counting –new compass –new sonars –smooth pwm

8. Hardware: a warning Please take extreme care in handling of all hardware –no loose connections –tidy soldering –careful charging –check static –if you are not 100% sure then ASK We will not be able to replace severely broken kits (you will have to transfer module)

9. First Week Handyboard + IC Aim to build a complete exploring robot by end of week 2 –will familiarise you with sensors and their properties –will give you practice in robot construction

10. Base Task Description Robot Rubbish Clear up –arena with four drop zones bottles (green) tennis balls (yellow) pepsi cans (blue) coke cans (red) squiggle ball (any) –collect and correctly deliver the rubbish 2 robots in each bout of 5 mins

11. Base Task Description Scoring scheme in assessment handout Two demos (1 public, 1 private) Best of two demos Competition score –influences demo mark –not the only factor Private demos on the 22n d, 24 th and 25 th November Public demo 30 th November 2-4pm Last Year’s results

12. Assessment Outline Assessment by demonstration and report Report must be –25 pages maximum –10 pt minimum Hand in 12 noon on 8 th December

13. Course Team Jeremy Wyatt Lecturer Noel Welsh Teaching Assistant Aleem Hossain, Arjun Chandra Demonstrators Ben Stone system software support Richard Pannell, Bert Dandy hardware support

14. What’s Easy is Hard Easy: expert systems, mathematics, chess Hard: seeing, language understanding, moving around, making a cup of tea, common sense moving around What’s easy for humans is hard for computers and vice versa. Why?

15. The Whole Iguana AI commonly studies aspects of intelligence separately: narrow domain high performance In 1976, philosopher Dan Dennett suggested putting it all together, but with a low level of performance

16. The Whole Iguana “... why not obtain one's simplicity and scaling down by attempting to model a whole cognitive creature of much less sophistication than a human being?... a turtle, perhaps... [but] considering the abstractness of the problems properly addressed in AI... one does not want to bogged down in the cognitive eccentricities of turtles if the point of the exercise is to uncover very general, very abstract properties that will apply as well to the cognitive organisation of … human beings. So why not make up a whole cognitive creature, a Martian three-wheeled iguana, say, and an environmental niche for it to cope with? I think such a project could teach us a great deal about the deep principles of human cognitive psychology …”

17. Experiments with vehicles Behaviour of agents was more complex than their mechanisms Behaviour depended on the environment as well as the agent Hard to infer mechanism from behaviour alone

18. Experiments with vehicles Valentino Braitenberg - “Law of uphill analysis and downhill invention” My Conclusion: synthesizing agents may have something to offer in understanding our minds

19. Why build robots to understand minds? All naturally occuring intelligence is embodied So robots are in some ways similar systems Robots, like animals exploit their environments to solve specific tasks “There are no general purpose animals … why should there be general purpose robots?” David MacFarland

20. Lessons from nature Gannets – wings half open to control dive Fold wings to avoid damage Not at a constant distance, but at a constant time Birds have detectors that calculate time to impact

21. Task specific robots Polly the tour guide exploits assumptions about environment to perform task quickly

22. Lessons from nature: 2 Other animals are capable of a surprising degree of manipulative ability e.g. Betty the crow who can make toolsBetty the crow make tools Sometimes we can use robots to test theories of how specific animals work e.g. cricket phonotaxis

23. Wrap up By synthesising intelligent robots we can address deep questions about the nature of intelligence Robots, like animals, are embodied We can use the task-environment dynamics to constrain our computational problems