Biorobotics: Using robots to model animals

Barbara Webb

2

Biology Robots

Why can’t we build a beetle? • Number and variety of sensory systems• Flexibility and integration of actions –locomotion, manipulation, construction, cleaning, defence, escape…• Power efficiency • Size • Robustness• Self reproducing, self growing, self repairing…

3

Biology Robots

How can we verify our hypotheses?4

Target systemTarget behaviourWorld

observing

Source

Simulation behaviour

generatingSimulationTechnology

representing

Predicted behaviour

predictingHypothesis

theorising comparing

interpreting

What does “model” mean?

5

“the best material model of a cat is another, or preferably the same, cat”

Rosenblueth & Wiener (1945)

What choices do we make when building a model?

6

Different dimensions of modelling

identity

medium

7

E.g. Hardware solution to localising sound

Cricket is small (1-2cm) relative to the sound distance (1-5m) and wavelength (5-6cm)

Good directional information - for a specific frequency8

When tested on the robot, can choose between sounds,

4.7Hz

4.7Hz

4.7Hz

6.7Hz

4.7Hz

6.7Hz

- preferring correct carrier frequency

9

Different dimensions of modelling

identity

medium

level

10

E.g. Algorithms for recognition and localisation

recognise recognise

compare

LEFT RIGHT

Compare onsets

LEFT RIGHT

recognise

compare

LEFT RIGHT

11

Temporal filtering in dynamic synapses

Mutual inhibition

Circuit based on identified neurons in cricket:

12

Robot brain neurons selective for syllable rates

BN1 BN2

13

Different dimensions of modelling

identity

medium

behavioural match

level

14

Hedwig & Poulet (pers. comm.)

Crickets steer to ‘unattractive’ song pattern if presented during attractive song

E.g. Dynamics of recognition and localisation

15

Hedwig & Poulet

16Onset of response to song Response to pulses after song

Hedwig & Poulet

17

recognise

compare

LEFT RIGHT

18

Different dimensions of modelling

identity

medium

structural accuracy

behavioural match

level

19

E.g. What is the mechanism of integration of phonotaxis and the optomotor response?

Bohm et al (1991)

20

Cricket appears to simply add the two turning tendencies

Bohm et al (1991)

21

Optomotor aVLSI chip

(Harrison & Koch 1999)

22

gp

PHONO

go

OPTO∫(visual motion)

∫(sound direction)

θ

Efferent copy

AdditivePost-integration

φ

23 24

Different dimensions of modelling

identity

medium

relevance

structural accuracy

behavioural match

level

25

E.g. questions raised by robot for further investigation in cricket phonotaxis

• If cricket compares onsets, should turn towards sound presented on one side just before other (experiment now done: apparently does not)

• Need data on the temporal characteristics of the turning response (recently found surprising results)

• Very difficult for robot to deal with the large amplitude range as it approaches sound – how does cricket do it?

Hedwig & Poulet (2004) -Crickets steer to every syllable, with latency of 50-100 ms 26

Different dimensions of modelling

identity

medium

abstractionrelevance

structural accuracy

behavioural match

level

27

PC104+

wireless ethernet

Compliant foot

Mast for tracker tethersKhepera robot

+ears circuit

Microphones

15cm radius wheg

60cm long chassis

E.g. ‘Whegs’ abstraction of insect tripod gaitHorchler & Quinn (CWRU)

28

Abstraction/detail and other dimensions

• Abstraction ≠ medium: e.g. whegs is an abstract physical model, could have more detailed computer simulation of insect walking

• Abstraction ≠ level: can have complex algorithmic models and simple neural models

• Detail ≠ accuracy: simple or complex model may or may not include the right mechanisms

• Abstraction ≠ generality: “Generality has to be discovered, it cannot simply be declared”Weiner 1995

29

Different dimensions of modelling

identity

medium

generality

abstractionrelevance

structural accuracy

behavioural match

level

30

cricket phonotaxis

other sensory systems

multimodal neurons

combining behaviours

integration

environment and task

body

other animals

small neural circuits

Functional significance of

low-level neural properties

alternative hypotheses

engineering approaches

spiking Short-term dynamics

Synaptic conductance

Model representation

local reflexes

task-matched sensing

active sensing

evolution

Details of movement

Leg control

insect brain architecture

feedback loopssensor fusion

forward models

context

learning

proto-cognition

E.g. starting with a specific system