Adaptive Robotics COM2110 Autumn Semester 2008 Lecturer: Amanda Sharkey

1. Adaptive RoboticsCOM2110 Autumn Semester 2008Lecturer: Amanda Sharkey

2. Robots in the news • 9:13am UK, Friday November 14, 2008 • “British scientists have come up with the first robot that can mimic a person's expressions simply by watching their face.” • A disembodied humanoid robotic head with video camera eyes and 34 internal motors under “Flubber” skin • Works with 10 human emotions (happiness, sadness, concern) • Can mimic human expressions that it recognises. • Bristol Robotics Lab (Chris Melhuish) at UWE • Robot head commissioned from David Hanson • Can scan and “understand” images at 25 frames per second.

3. 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 • Lect 6: Swarm Robotics (reasons for, biological inspiration, local control and communication, self-organisation and emergence).

4. Robots as Biological Models • Robots can be used to investigate biological questions • And to test hypotheses

5. Biorobotic Modelling • What is Biorobotics? • The intersection of biology and robotics • Common ground between robots and animals: • Both are moving, behaving, systems • Both have sensors and actuators • Both require an autonomous control system to carry out tasks in a dynamic world.

6. To count as biorobotic modelling a system should • Be robotic (physically instantiated) • Be biological (address a biological hypothesis, or demonstrate understanding of a biological system). (Webb, 2001)

7. Biorobotics: “understanding biology to build robots and building robots to understand biology” (Webb, 2001)

8. Synthetic modelling • Understanding by building • Synthetic vs analytic • Analytic approach in empirical sciences – performing experiments on existing systems (human, ant, brain region) and analysing results • Synthetic: Idea of creating an artificial system that reproduces certain aspects of natural system

9. Robots have (arguably) replaced computers as the metaphor to develop and test models of biological intelligence

10. Early example – “electric dog” (Seleno) • Loeb and animal taxis or forced movement • Hammond and Miessner,(1912) “electric dog” • Loeb saw the “artificial heliotrope machine” as confirmation of his theories

11. Advantages of biorobotic models • Robotic implementations force scientists to be very concrete in specifying the design of biological system • Robot operating in the real world constrains the choice of models • Can help to produce testable hypotheses • Robot models allow scientists to study interaction of model with the environment • Simulations may introduce misleading simplifications

12. Webb (2001) • 7 dimensions along which robotic models of biology differ • Relevance: whether model tests and generates hypotheses applicable to biology • Level: the elemental units (atoms – society) • Generality: the range of biological systems the model can represent • Abstraction: the complexity relative to the original • Structural accuracy: how well the model represents the actual mechanisms underlying behaviour • Performance match: to what extent the model behaviour matches the target behaviour • Medium: the physical basis by which the model is implemented.

13. A robotic cricket: Song Recognition and Localisation • Lund, Webb and Hallam (1998) • Phonotaxis in the cricket • Lund, H.H., Webb, B., and Hallam, J. (1998) Physical and Temporal Scaling Considerations in a Robot Model of Cricket Calling Song Preference. Artificial Life, 4, 1, 95-107. • Webb, B., and Scutt, T. (2000) A simple latency-dependent spiking-neuron model of cricket phonotaxis. Biological Cybernetics, 82: 247-269.

14. Female crickets recognise males’ songs and approach them. • They have an ear in each foreleg that produces direction-dependent differences in response amplitude • Turns to strongest response? • But – only approach males of the same species with characteristic song.

15. Field cricket

16. Ground cricket

17. Tree cricket

18. Mole cricket

19. Bush cricket

20. Scaly cricket

21. Taxis, or approach to a sensory source • When modelled, often abstracted as a source signal, whose value decreases with distance, and an animat that can sense that value and use it to control movement to the source. • E.g Braitenberg’s vehicle 2 • But does this capture the essence of the behaviour? • Lund et al (1998) argue it does NOT

22. They say … • Abstract model ignores the real physics of signal propagation and detection • Reasonable approximation of light but not of other modalities • E.g. chemotaxis, where odour signal is highly dispersed and carried by currents in environment • Phonotaxis: sensors don’t respond exclusively to relevant signal • Emphasis on physical properties of task and agent

23. Unidirectional communication • Male cricket stridulates • Rubbing wings together, or a leg against a wing. • Female cricket responds by moving towards the source of the song.

25. Earlier implementation: LEGO robot – but slow and comparison to cricket data difficult. • Here, new robot and revised models • band-pass selectivity • using signals that are temporally identical to crickets.

26. The female cricket has 4 auditory openings • An ear (tympanum) on each foreleg • An auditory spinacle (hole) on each side of the frontal section • The 4 openings are linked by tracheal tubes • Sound reaches tympani through air • And via internal tubes from other auditory openings

28. Sounds transduced from tympani are a combination of delayed and filtered signals • The delays and filters improve cricket’s ability to discriminate the arrival direction of its mate’s song. • Sounds arriving from same side as tympanum are delayed (by internal structure) to arrive in antiphase to ipsilateral ear, and in phase with contralateral ear. • Sounds subtracted, so intensity at ipsilateral (same side) ear is enhanced

29. Female crickets home in on potential partners by listening to their chirping (phonotaxis) Sound is made of short bursts or syllables. She moves towards the source of the song; But she only responds to calling songs with syllable rates within a certain bandpass. Are two processes implicated? (i) recognition of cricket song (ii) localisation system to approach source of the song.

33. The neural model • Input from auditory sensors is fed into a neuron on each side (N1) • Activation can flow from N1 to another neuron N2 that feeds activation to the motor on that side. • Activation modelled with a leaky integrator with associated threshold (T high) before firing. • When it is fired, activation has to decay below a lower threshold (T low) before it can fire again • NB N1 and N2 are not intended to correspond to specific neurons in the cricket, but represent processes carried out by small numbers of neurons.

35. Phonotaxis experimental results The robot is attracted to the male cricket calling song. It also discriminates between calling songs with the right carrier frequency (4.7kHz) and those with other carrier frequences (e.g. 6.7kHz) Neural circuitry is set up to respond to species-specific syllable repetition interval

38. The robot’s response to different syllable rates (little response when syllable intervals are less than 20 ms and more than 60 ms) is the same as in the real cricket.

39. Their conclusions • Explains why crickets respond only to certain calls • Mechanism implements the bandpass filtering found in crickets. • With N1 neurons acting as low pass filter, and N2 acting as high pass filter. • “high level” model of cricket controller – N1 and N2 don’t correspond to specific neurons in cricket, but represent processes that may be carried out by 3-10 neurons in the cricket prothoracic ganglion and brain.

40. Mechanism underlying phonotaxis in robotic cricket is surprisingly simple • Shows that there is not need for two neural control systems • One to recognise the call • One to locate it. • Here the two systems are one

41. “A generic simulation may tell us little about real problems in approach behaviour. By investigating a specific biological system and modelling it at a level of detail driven by biological questions, we gain more sophisticated insights into the real problems of sensorimotor control” • Lund et al (1998)

42. But it is also possible to investigate phonotaxis in real crickets …..

43. Hedwig, B., and Poulet, J. (2004) in Nature • Using tracking ball, could look at cricket responses to chirps presented from left and right of cricket • Response was very rapid • - “reflex like reactive steering towards individual sound pulses” • Other methods (bandpass filtering brain neurons, or template matching), would require at least two consecutive sound pulses • But recognition process may modulate steering responses over time.

44. Biorobotics and …. • Biological relevance: biorobotics more likely to assume a particular biological target than biologically inspired robotics • Level: usually sensor transductions considered • Generality: usually biorobotic models are more specific e.g modelling sound localisation of crickets. But general principles can emerge (I.e. idea of exploiting timing properties of neural firing)

45. Abstraction. Usually modelling simplifies some aspects – e.g. in the robot cricket model there is minimal representation of biological details in motor control of cricket • Structural accuracy: how well do mechanisms reflect real mechanisms in target? How would we know? Usual aim is to build complete model that connects sensing to acting.

46. Performance Match: to what extent does model behave like the target? How will model’s performance be assessed? Little possibility of falsifying model - if match is poor this could be attributed to measurements, not model - depends on interpretation of model behaviour - parameters of model can be tuned until they match.

47. Medium: what is simulation built from? • Biorobotics, emphasis on “complete model” that produces an output, and on understanding the environmental conditions

48. What does a biorobotic model tell us? • Can we ever really validate a model? • Do models ever tell us something we didn’t already know?

49. Modelling: involves the correspondence between a real target system and something else. Problems: underdetermination. There are many possible mechanisms that could underlie a behaviour. If two systems behave the same, it does not follow that the cause or mechanism of the behaviour is the same. But, can show that a proposed mechanism is sufficient to produce the behaviour

50. Can we assume that this model of cricket phonotaxis represents the mechanism underlying the cricket’s behaviour? Reasons for doubting: