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Biorobotics: Using robots to model animals
Barbara Webb
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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…
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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?
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“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?
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Different dimensions of modelling
identity
medium
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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
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Different dimensions of modelling
identity
medium
level
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E.g. Algorithms for recognition and localisation
recognise recognise
compare
LEFT RIGHT
Compare onsets
LEFT RIGHT
recognise
compare
LEFT RIGHT
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Temporal filtering in dynamic synapses
Mutual inhibition
Circuit based on identified neurons in cricket:
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Robot brain neurons selective for syllable rates
BN1 BN2
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Different dimensions of modelling
identity
medium
behavioural match
level
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Hedwig & Poulet (pers. comm.)
Crickets steer to ‘unattractive’ song pattern if presented during attractive song
E.g. Dynamics of recognition and localisation
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Hedwig & Poulet
16Onset of response to song Response to pulses after song
Hedwig & Poulet
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recognise
compare
LEFT RIGHT
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Different dimensions of modelling
identity
medium
structural accuracy
behavioural match
level
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E.g. What is the mechanism of integration of phonotaxis and the optomotor response?
Bohm et al (1991)
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Cricket appears to simply add the two turning tendencies
Bohm et al (1991)
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Optomotor aVLSI chip
(Harrison & Koch 1999)
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gp
PHONO
go
OPTO∫(visual motion)
∫(sound direction)
θ
Efferent copy
AdditivePost-integration
φ
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Different dimensions of modelling
identity
medium
relevance
structural accuracy
behavioural match
level
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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
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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)
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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
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Different dimensions of modelling
identity
medium
generality
abstractionrelevance
structural accuracy
behavioural match
level
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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
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