Action Causes Perception Causes Action:
From Sensory Substitution to Situated
Robots
Lecture 3+4, Unit 5
NUCOG Seminar: Action, Perception, Motivation
Akureyri, Iceland 10.2.-20.2.2006
Marieke Rohde
Centre for Computational Neuroscience and Robotics
University of Sussex
Recapitulation
•
Situated and Embodied View:
– The closed sensorimotor loop
– The rejection of (a priori) internal localisation of cognitive function
– Sensorimotor coordination as reciprocally causal process.
•
Empirical research
– Perceptual suppleance (sensory substitution)
– Change blindness
– Delay experiments
•
Sensorimotor contingencies
– Descriptive concepts for a situated view.
– Can be used to explain cognitive phenomena and faculties (e.g. perceptual
modalities) without localising meaningful cognitive phenomena
This module
Tuesday:
1.
History and Motivation
2.
The Importance of Situatedness: Empirical Evidence
3.
A Sensorimotor Account
Today:
4.
Robotics
5.
The Question of Value
6.
Conclusion
4.) Robotics
Shakey
•
“Shakey was the first mobile robot to
reason about its actions”
•
Shakey implemented the perception,
planning, action approach.
–
Detailed world model
–
Three levels of complexity
http://www.sri.com/about/timeline/sha
key.html
Video:
http://www.ai.sri.com/movies/Shakey.ram
Braitenberg
• Cunningly simple
• No internal state – yet
„cognitive“ behaviour
• One controller –
radically different
behaviours
Braitenberg, V.: „Vehicles. Experiments in Synthetic
Psychology.“ illustrations by Maciek Albrecht, MIT Press, 1984
The Case of Walking
Why is walking so easy for us, and so difficult for
robots?
(see e.g. Honda’s Humanoid ASIMO:
http://world.honda.com/ASIMO/ )
Passive Dynamics
• Passive dynamic walking at Cornell:
http://www-personal.engin.umich.edu/~shc/movies/passive_angle.mov
• Slowly introducing activity (power) in simulation:
http://www.droidlogic.com/sussex/dphil/movies/fullspine3_front.mov
• Take inspiration from airplanes: Gradually add control to gliding.
A Lesson from Robotics
•
In Shakey, a theoretical model was „acid tested“ (with little success)
•
The actual problems had not been recognised.
•
A controller that is no controller at all outperforms Shakey effortlessly
•
Close coupling instead of „dead reckoning“
•
Detail through detailed representation.
•
Classical approaches focus on what people are bad at and computers are
good at (logics, mathematics, chess...). They fail to account for what people
are good at, but computers are bad at.
•
The rise of Behaviour Based Robotics: e.g. Brooks: „Intelligence Without
Reason“, subsumption architectures.
Evolutionary Robotics
Advantages
• Integrated sensorimotor systems
– Close coupling between agent and environment (which is typically
bypassed or modelled very poorly).
• Control (and minimise) prior assumptions (prejudices)
– About what internal structure is necessary to solve a task
– About what kind of functional decomposition underlies the mastery of a
task
– About which strategy is applied to solve a task
– Goes beyond human ingenuity, particularly with respect to complex
nonlinear dynamics
Minimally Cognitive Behaviour
Beer, R.D. (2003).
• Minimally cognitive Behaviour – what and why
– Raises genuine cognitive interest
– Minimal complexity on which we can build up
systematically
– Dynamical Explanation of brain, body, world
interaction
– „Intellectual Warm-Up“, „Frictionless Brains“
Categorical Perception
• Task
– Circular Agent
– Recurrent Neural Network
Controller (CTRNN)
– Move left and right
– Distance sensor array
– Objects fall from the sky
– Catching Circles, avoiding
Diamonds
– (symmetry)
Behavioural Explanation
• Foveate Object
• Scan Left and Right
• Circle: scanning
movements smaller
and smaller
• Diamond: large
avoidance movement
What the agent sees...
„Carrot on a stick“
„Psychophysics“
• Labeling and
Discrimination
• Discrimination Criteria
(Width!)
Psychophysics
• When is the
decision made?
Dynamical Explanations
• Hardcore Mathematics
• Dynamics of agent environment
system
Memory
• Izquierdo-Torres & Di Paolo
(2005):
– Is a reactive agent capable of
only solving reactive tasks?
– Reactive task: what is to do is
immediately obvious from the
sensory data
Memory
•
Izquierdo-Torres & Di Paolo (2005):
– Same Task, same settings (symmetry etc.)
– Feed Forward network without decay
– Perfect mastery, even with respect to „psychophysics“
– An embodied and situated agent is never purely reactive: Agents modify their
position with respect to the objects and thereby partially determine their sensory
perception at the next time step
More Cases
• I will just brush over these...
Active Vision
•
Coevolution of active vision and feature selection (Floreano et Al. 2004)
– A feedforward network masters complex behaviour relying on vision
•
Extension: Development under motor disruption (Floreano et Al. 2004b)
– Analogous to Held‘s Kitten caroussel
Learning Dynamics
•
Tuci et Al. (2002)
– Recurrent neural networks are dynamical
networks, i.e. They have neural state
– Neural network learning is normally thought
to happen through a different process (i.e.
Synaptic learning)
– Learning in a fixed weight neural network
– Floor sensor, light sensor: find out at the
beginning of 14 trials if you are in a
„landmark near“ or „landmark far“
environment
Communication and Social
Interaction
• Matt Quinn (2001):
– Origins of Communication: Making signal only if
it is understood. But: the first time made it will
not be understood.
– Dedicated Channels? No prior assumptions
about how to communicate
– Homogenous population allocates roles
(„leader“, „follower“)
– Minimal sensory and motor equipment (Only
distance, 5 cm range, noisy)
Where‘s the Talking Robots?
• People tend not to be impressed with this
• I am not impressed with AIBO, ASIMO, the Sony Humanoid, ...
(well engineeringwise, I am impressed)
• The biggest issues in current cognitive robotics (e.g. Robocup) are
still the ones that we get for free. Timing issues: Blur, Slip, Delays...
• Manufacturing robots are normally controlled according to dynamical
principles
What Does that Prove?
• Following up on David‘s question
– „When an ER experiments replicates some
cognitive capacity of a human or
animal,typically in simplistic and minimal form,
what conclusions can be drawn from this?“
(Harvey et Al., 2005)
Answers
• Harvey et Al. (2005): Existence proof
– sufficient conditions to generate behaviour x
– catalysing theoretical re-conceptualizations
– Facilitating the production of novel hypotheses
• Di Paolo et Al. (2000): Opaque Thought Experiment
– Results follow from Premises – but in a non obvious way
– Empirical Flavour: must be observed and understood
– Go beyond human ingenuity and thus
• Make a stronger case
• Can uncover novel concepts, relations etc. to be incorporated in a theory
Conclusions so far
• What all these experiments show is that behavioural and cognitive
phenomena that are typically put in distinct boxes in psychology, can
be realised by a system whose mechanic structure is very different
(orthogonal) to the structure of the behaviour space.
• These mechanisms have a tendency to be more efficient (i.e.
computationally cheaper), and exploitation of a close coupling to the
environment is part of this advantage.
• My question: Why would nature/evolution box up functional
mechanisms?
5.) Values
The Problem
• Why is light meaningless to a
Braitenberg vehicle?
• What constitutes genuine purpose,
genuine values, genuine
intentionality?
A Look at Biology
• Living organisms have genuine purposes. They care for their
survival. They have to, otherwise they would not live.
• Survival is not „for free“ as it is in the case of the robot.
• They cannot be reprogrammed
• What is good or bad to them is not down to interpretation.
– Can you redefine what is reward and what is punishment for a living
organism?
– Can there be conventions about what is harmful for a living organism?
I could never put it as nicely…
• “the ill person that cannot express himself anymore,
animals, yes, even a paramecium that cramps before it
is killed by the picric acid dribbled under the cover slip,
the saddening look of a limp plant, the foetus that
defends itself with hands and feet against the
instruments of the doctor - they all present the meaning
of that what happens to them. The meaning is explicitly
evident in the gestures.“ (Weber, 2003. p. 149, my
translation)
Autopoiesis
•
Maturana and Varela (1980): operational definition of
the living.
•
Definition:
a network of processes of production (synthesis and destruction)
of components such that these components:
1.
continuously regenerate and realize the network that produces them,
and
2.
constitute the system as a distinguishable unity in the domain in which
they exist
(Weber & Varela, 2002, p. 115)
Is that enough?
• Autopoiesis just accounts for robustness.
– What is dying? Illness? Stress?
– A merely autopoietic system has no reasons to
improve the conditions for its continued existence.
Adaptivity (Di Paolo, forthcoming)
“a system’s capacity, in some circumstances, to
regulate its states and its relation to the
environment with the result that, if the states are
sufficiently close to the boundary of viability,
1. Tendencies are distinguished and acted upon
depending on whether the states will approach or
recede from the boundary and, as a
consequence,
2. Tendencies of the first kind are moved closer to or
transformed into tendencies of the second and so
future states are prevented from reaching the
boundary with an outward velocity.”
Values
• Metabolic value, as an end, and a criterion for
judgment seems reasonable.
• Maybe it is enough to explain the behaviour of
the most simple organisms.
• However, not all our judgments or all our actions
seem to be measurable against metabolic value.
• Do all values derive from metabolic value?
Our Definition of Value
“We propose to define value as the extent to which a
situation affects the viability of a self-sustaining and
precarious process that generates an identity”
(Di Paolo & Rohde, work in progress)
• Note: There is reciprocal causality!!!!
• Which other values could there be?
• Do non-metabolic values parasite on metabolism?
• Could there be values without a metabolism?
Value System Architectures
• Edelmann‘s Theory of neuronal group selection (e.g. Edelmann
(1989), Sporns & Edelmann (1993))
– Neural circuits are selected due to principles of Darwinian evolution
during lifetime
– Selection through a value signal
– E.g. Reaching: „good“ if hand close to target
– Reinforcement learning with internally generated reinforcement signal
• Very popular with Pfeifer et Al. (e.g. Pfeifer and Scheier 1999)
The Value System
• ``[if] the agent is to be autonomous and situated, it has to
have a means of `judging' what is good for it and what is
not. Such a means is provided by an agent's value
system.'' (Pfeifer and Scheier 1999)
• ``already specified during embryogenesis as the result of
evolutionary selection upon the phenotype'' (Sporns and
Edelmann 1993).
Rephrasing it:
• There is a structure that knows what is good and bad (a priori)
• The rest of the organism/learning mechanism is ignorant and obeys
blindly
• This localised structure, by necessity needs to have dedicated input
and output channels
• Value systems themselves do not learn, they control learning
(functional division)
• Or if, they learn through a „higher level“ value system (regressus ad
infinitum?)
A „VISTIGIAL GHOST IN THE MACHINE“!!!! (Rutkowska 1997)
What is wrong with value systems?
The principle objection:
– Values are arbitrary.
– Values are generated seperately.
– Values are specified a priori.
– Values are not subject to change themselves.
– What happened to the reciprocal causality?
Think about: sensory substitution, goggle experiments...
Think about: social/abstract values and the requirement for
„simple criteria of salience and adaptiveness“ ()
These are not genuine
values!!!
What else is wrong with value
systems?
Are value systems good to model values?
– Investigation of „pseudo values“
– Models and idealisation: To remove gravity from a model of balloon
flight is simply to do away with the original problem we wished to solve.
• So what is wrong?
– Vulnerability
– Generality/Specificity trade-off
– Buckpassing explanatory burden
– False dilemmas: analoguous to nature/nurture divide (Oyama 1985)
– The impossibility of novel values
Damasio
• ``[somatic markers] help us thinking, by illuminating
some (dangerous or beneficial) options in the right way
so they are quickly removed from further reasoning. You
can imagine this as an automatic system for the
evaluation of predictions'' (my translation from German
translation of Descartes error) :$
• ``we are born with the neuronal mechanism necessary to
generate somatic states facing certain classes of stimuli
- the apparatus of primary emotions.”
Emotion systems
• conceived as forming a complementary system to colder or more
detached cognitive processes
• kind of “early warning system” that directly monitors bodily
conditions to generate states that modulate all kinds of interactions
and internal dynamics.
• Again:
– a priori built-in rules
– emotional states
– functional division between the emotion system and other emotion--free
cognitive processes
Just to make this very clear:
• Nobody denies that such mechanisms can and possibly
do work in situations that rely on ontogenetically or
phylogenetically preestablished situations.
• Both value and emotion systems provide the other
cognitive mechanisms with information of the relative
relevance of their activities and future choices.
• This cannot account for the generation of novel values.
How else could you model values
• Reciprocal Causality between value and
value appraising agent:
– Dynamics
– Plasticity
– Situatedness
– No functional separation
Evolutionary Robotics
• First step: The phototactic
homeostatic robot. (Di
Paolo 2000, 2003)
Trying to get a grip on „value
signals“
• The fitness evolving robot
– Evolve robot to perform a task (phototaxis) and a signal that
represents a fitness estimate.
– The fitness estimate is a standard neuron
– Give the robot an environment that requires adaptation (e.g.
Sensor swapping)
How does the behaviour relate to the „value signal“?
How does the „value signal“ relate to the behaviour?
6.) Conclusions
Summary
• The situated and embodied approach to cognition and how it differs
from the „cognition as information processing“ metaphor.
• How empirical research supports the situated and embodied view
– Perceptual perturbations
– Perceptual suppleance (sensory substitution)
– Change blindness
– Delay experiments
• The concepts and language that Noe, O‘Regan and Hurley
contribute to the situated, embodied and dynamical study of
cogntion. In particular, the idea of sensorimotor contingencies and
the mastery of the laws of sensorimotor contingency.
Summary
• How findings from robotics research, particularly from evolutionary
robotics, can inform cognitive science
• How behaviour can be explained dynamically (i.e. Using dynamical
systems theory)
• How true genuine value appraisal cannot be produced by a box of
judgment rules
• What biology teaches us about metabolic (and other?) values.
• How possibly to approach a complex and rich phenomenon like
value appraisal through computational modelling.
Thanks are Due to
• Ezequiel Di Paolo
• Sarah Angliss
• Inman Harvey
• Bill Bigge
Any questions?
References
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Beer, R.D. (2003). The dynamics of active categorical perception in an evolved model agent (with commentary and
response). Adaptive Behavior 11(4):209-243.
Braitenberg, V.: „Vehicles. Experiments in Synthetic Psychology.“ illustrations by Maciek Albrecht, MIT Press, 1984
Collins, S.: Passive Dynamic Walking at Cornell University (retrieved 13.2.2006). Information:
http://ruina.tam.cornell.edu/hplab/pdw.html
video: http://ruina.tam.cornell.edu/hplab/downloads/movies/Steve_angle.mov
Damasio, A. R:. Descartes' Irrtum. Fühlen, Denken und das menschliche Gehirn. München: DTV, 2001
Di Paolo, E. A., Adaptive Systems lecture presentations, University of Sussex (UK), Spring Term 2006.
Di Paolo, E. A., (2005). Autopoiesis, adaptivity, teleology, agency. Phenomenology and the Cognitive Sciences.
Forthcoming.
Di Paolo, E. A., (2003). Organismically-inspired robotics: Homeostatic adaptation and natural teleology beyond the
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Sociality, Advanced Knowledge International, Adelaide, Australia, pp 19 - 42.
Di Paolo, E. A., Noble, J., & Bullock, S. (2000). Simulation models as opaque thought experiments. In Bedau, M.
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Harvey, I.: Various presentations and lecture material
References
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Harvey, I., Di Paolo, E., Wood, R., Quinn, M. and Tuci, E. A. (2005). Evolutionary Robotics: A new scientific tool for
studying cognition. Artificial Life, 11(1-2):79-98.
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