Adaptive Robotics COM2110 Autumn Semester 2008 Lecturer: Amanda Sharkey

1. Adaptive RoboticsCOM2110 Autumn Semester 2008 Lecturer: Amanda Sharkey

2. So far … • Lect 1: what is a robot? Brief history of robotics • Early robots, Shakey and GOFAI, Behaviour-based robotics • Mechanisms and robot control (and biological inspiration) • Lect 2: Grey Walter, Brooks and Subsumption Architecture. • Lect 3: Adaptation and learning • Lect 4: Artificial Neural Nets and Learning • Lect 5: Evolutionary Robotics

3. “Robots in the news” Honda – new wearable assisted walking gadget Designed to support bodyweight, reduce stress on the knees and help people get up steps and stay in crouching positions. To be used by workers in auto factories To be tested next month with assembly-line workers Based on technology developed for their Asimo robot

4. Phoenix: NASA Martian probe • Has come to the end of its mission • Not enough light to recharge batteries, and winter • Has been on Mars for 5 months – sent back 25000 images

5. Due by Monday 17th Nov at 11 am • Write an essay (1500-2500 words) on one of the following topics. You should use the lectures as a starting point, but also research the topic yourself. Plan your answer. Include a reference section, with the references cited in full.  1. Identify the main characteristics of Behaviour-based robotics, and contrast the approach to that of “Good old-fashioned AI”. 2. To what extent did Grey Walter’s robots, Elsie and Elmer, differ from robots that preceded, or followed them. 3. Explain how the concepts of “emergence” and “embodiment” are related to recent developments in robotics and artificial intelligence.

6. Collective Robotics Swarm Robotics

7. Collective robotics • Why invest in collections of robots, why not build a reliable individual robot? • Task difficult (or impossible) for one robot • Can be performed better by many • Redundancy – task more likely to be completed • Simplicity – many cheaper robots instead of one expensive one.

8. What kinds of collections? Possibilities range from - remote controlled robots - centrally controlled robots - completely autonomous robots

9. Cao, Fukunaga and Kahn (1997) Cooperative mobile robotics: antecedents and directions. Autonomous Robots, 4,1, 7-27.

10. Advantages of robot collectives shown in • Environmental exploration • Materials transport • Coordinated sensing – collective cooperates to provide maximal sensor coverage of moving target. • Robot soccer • Search and Rescue

11. Swarm Robotics • Taking a swarm intelligence approach to robotics

12. Swarm intelligence Swarm intelligence is “any attempt to design algorithms or distributed problem-solving devices inspired by the collective behaviour of social insect colonies and other animal societies” Bonabeau, Dorigo and Theraulaz (1999)

14. Natural swarms • Decentralised – no-one in control • Individuals are simple and autonomous • Local communication and control • Cooperative behaviours emerge through self-organisation e.g. repairing damage to nest, foraging for food, caring for brood

16. Self-organisation • Organisation increases in complexity, without external guidance • Self-organising systems often display emergent properties • “self-organisation is a set of dynamical mechanisms whereby structures appear at the global level of a system from interactions among its lower-level components. The rules specifying the interactions among the system’s constituent units are executed on the basis of purely local information, without reference to the global pattern, which is an emergent property of the system rather than a property imposed upon the system by an external ordering influence” (Bonabeau, Dorigo and Theraulaz, 1999)

17. Emergence • An emergent property, e.g. pattern formation, from more basic constituents • An emergent behaviour can appear as a result of the interaction of components of the system • E.g. flocking, or organisation of ant colony

18. Real life example of self-organised behaviour in humans • Emergence of paths across grassy area • Most popular paths are reinforced • Counter –example e.g. a team of carpenters building a house….not self-organised.

19. Swarm robotics • Inspired by self-organisation of social insects • Using local methods of control and communication • Local control: autonomous operation • Local communication: avoids bottlenecks • Scalable – new robots can be added, or fail without need for recalibration • Simplicity – cheap, expendable robots • Self-organisation • Decentralisation

20. Disadvantages of centralised control and communication. • Central control: failure of controller implies failure of whole system • Robot to robot communication becomes very complex as number of robots increases. • Communication bottlenecks • Adding new robots means changing the communication and control system

21. Applications of swarm approach Some tasks are particularly suited to group of expendable simple robots e.g. - cleaning up toxic waste - exploring an unknown planet - pushing large objects - surveillance and other military applications

22. What issues are investigated? • Weak AI questions: • E.g. how can complex behaviour, such as cooperation, emerge as a result of interactions between simple agents and their environment? • Biological modelling – better understanding of social insects for example. • Biological inspiration – emulating behaviour and capabilities of biological systems

23. Cooperation and communication • Examples of communication in cooperative systems: • Increasing sophistication…. • Bacteria • Ants • Wolves • Non-human primates • Humans

24. Bacteria • Live in colonies • Explicit chemical signals mediate their ability to cooperate. • E.g. Mycobacteria assemble into multicellular structures known as fruiting bodies. • Bacteria emit and react to chemical signals

25. Ants • Also termites, bees and wasps • Display cooperative behaviour e.g. pheromone trails to food source Chance variations that result in shorter trail are reinforced at faster rate. Can find optimal shortest path Stigmergic communication.

26. Wolves • Territory marking through repeated urination on objects on periphery of territory • Also more sophisticated communication directed at particular individuals • Specific postures and vocalisations

27. Non-human primates • Sophisticated cooperative behaviour Higher primates can represent the internal goals, plans, dispositions and intentions of others, and to construct collaborative plans jointly through acting socially.

28. Humans • Many forms of communications – including written and spoken language • Many forms of cooperation, from basic altruism to cooperative relationships where we exchange resources for mutual benefit

29. Focus of interest here: • Emergent cooperation e.g. social insects: ants, bees, wasps, termites Stigmergic communication: one of the mechanisms that underlies cooperation.

30. Swarm robotics Biologically inspired by social insects - emergent complex behaviour from simple agents • Swarm Intelligence Principles: • Autonomous control • Simple agents (debateable – swarms of helicopters?) • Expendable, fast and flexible responses • Local communication • Scalable • Decentralised • Use and exploration of stigmergy

31. Mystery: cooperative behaviour when insects seem to work alone

34. individual insect responds to changes in environment created by itself or others • Grassé (1959) – stigmergy • Indirect social interaction via the environment

35. E.g. Termite nest building • Building arches • Termites make mudballs, which they deposit at random. Chemical trace added to each ball • Termites prefer to drop mudballs where trace is strongest. • Columns begin to form • Deposit more on side nearest to next column – eventually leads to formation of arch.

36. Example paper: Holland and Melhuish (1999) • Holland, O., and Melhuish, C., (1999) Stigmergy, self-organisation and sorting in collective robotics. Artificial Life, 5, 173-202.

38. Example of ant brood sorting “The eggs are arranged in a pile next to a pile of larvae and a further pile of cocoons, or else the three categories are placed in entirely different parts of the nest…if you tip the contents of a nest out onto a surface, very rapidly the workers will gather the brood into a place of shelter and then sort it into a different pile as before (Deneubourg, et al, 1991)

39. Franks and Sendova-Franks (1992) Brood sorting of Leptothorax unifasciatus - brood items sorted into concentric rings of progressively more widely spaced brood items at different stages of development.

40. Use of simulations Deneubourg et al (1991) “The dynamics of collective sorting: Robot-like ants and ant-like robots” • Showed agents could use stigmergy to cluster scattered objects of a single type, and to sort objects of two different types • For sorting – agents needed short-term memory to sense local density of different types of brood items and to know the type of brood item they were carrying. • But – a simpler solution can be found with physical agents – greater exploitation of real world physics.

42. Holland and Melhuish experiments: • Small U-bot robots, with infrared sensors, and gripper designed to sense, grip, retain, and release frisbees. • When robot moves forward, frisbee remains in gripper • When robot reverses, frisbee left behind, unless pin extended to keep it in place • When 2 or more frisbees pushed into, this triggers microswitch in gripper – not triggered when pushing or bumping into 1 frisbee.

45. Exp 1: how many U-bots in arena without too many collisions • Exp 2: Simple rule set Rule 1: if (gripper pressed and object ahead) then make random turn away from object -> ie turn away from boundary Rule 2: if (gripper pressed and no object ahead) then reverse small distance (dropping the frisbee) and make random turn left or right -> ie has encountered another frisbee. Rule 3: go forward

46. 44 frisbees placed across the arena • 10 robots released. • Frisbees gradually collected in small clusters – after 8 hours 25 mins, a cluster of 40 frisbees formed. • Frisbees taken from intermediate clusters if struck at an angle without triggering gripper

48. Experiment 5: sorting and pull-back algorithm • Plain yellow frisbees and black and white ring frisbees • Pin-dropping mechanism applied to plains • Rule 1: if (gripper pressed and object ahead) then make random turn away from object • Rule 2: if (gripper pressed and no object ahead) then • If plain lower pin and reverse for pullback distance raise pin reverse small distance (dropping frisbee) make random turn left or right Rule 3: go forward

49. - now if robot is pushing a plain frisbee and hits another, or if not pushing frisbee and collides with another plain in a cluster, the plain will be dragged backwards and dropped away from contact point. • Result (after 7h 35 m): central core of 17 ring frisbees with 11 plains and 4 rings round outside. • I.e. annular sorting, based on simple mechanism • - Example of seemingly complex behaviour (sorting) emerging from the application of simple rules.