Download Evolving Robot Intelligence

Evolving Robot Intelligence
Guszti Eiben
VU University Amsterdam, NL
University of York, UK
Impact Robotisering Congress, Amsterdam, 12-11-2015
Talk overview
• The (near) future of AI
• Evolutionary robotics
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What is it & why would you want it
How does it work
What can it do
What does it mean for us (short term, mid term, long term)
• Take home messages
The AI winter sleep is over
Summer is coming
The coming out of AI
AI becomes audible
The next wave of AI is …
Collective
Embodied
Population, group, network, IoT
Actuated devices, robots
Adaptive
Tamed
Learning, self-improving
Human in-the-loop, moderated autonomy
The next wave of AI is …
Collective
Too complex?
Embodied
Can hurt?
Adaptive
Unpredictable?
Tamed
Under control?
Big dreams are changing
thinking
machines
2000
(inter)acting
machines
2050
Intelligence & embodiment
intelligence (narrow)
Environment + body + mind  behavior
intelligence (broad)
Intelligence & evolution
Evolution can create intelligence
Artificial evolution can create artificial intelligence
Artificial evolution
Can solve hard problems
Can cope with changes
Can deliver original
solutions
Evolutionary robotics: What
• Mainstream robotics:
Aims to generate good behavior for a given robot
Good design
• Evolutionary robotics:
2nd order
engineering
Aims to create general, robot-generating algorithms
Good designer
Evolutionary Robotics: Why
• To solve problems too hard for traditional
engineering approaches
• To provide on-the-fly adaptation for robots 
(re)program robots without humans around
• To surprise us
• To create new forms of artificial life & intelligence
• To do research into evolution and embodied AI
Question:
What is the optimal body type for football?
Cute
Antropo-chauvinist
Question:
Given a body, what is the optimal
control mechanism?
Uncanny, the end of FOOTball
Genetic code
Genotype
Head_1 = 32, 81, 5
Wheel_1 = A, 1
Wheel_2 = A, 41
Wheel_3 = B, C, 3.141
Camera = rot(29)
WiFi = comm_98
Body = M[block_1, 2 ;
block_2, 4,5 ;
block_3, 21 ;
block_6, 8 ;
cilinder(4) ]
Light = R / G / B, LED*
Mic = sound_x
....
Phenotype
Robot population
Reproduction
MAM: RED CODE
DAD: BLUE CODE
KID: PURPLE CODE
Selection
Example: evolving obstacle avoidance
De Volmaakte Mens, HUMAN-VPRO TV, 2015
Brain crossover = social learning
Robots can employ “telepathy”  teaching without language or imitation
From learning robots
to teaching robots
DREAM project, http://robotsthatdream.eu/
Example: evolved drone swarm
Flying UAV networks to find POIs (e.g., survivors through their cell phones)
SMAVNET project, EPFL
Example: recovering from damage
Evolved locomotion strategy to recover from physical injury
Cully et al., Robots that can adapt like animals, Nature, 2015
Example: evolving realistic locomotion
Simulated bipeds, natural gaits are discovered through evolution
Geijtenbeek et al., ACM Trans on Graphics, 2013
Evolving bodies
MOM
DAD
KID
State of the art 2015
3D printer + fixed organs
“Boy meets girl” scenario
Docu: How I met your father
VU Amsterdam, Robot baby project, 2015
Ethics
KILL SWITCH
EvoSphere
Opportunities
Short term < 5 years
•
Evolved
intelligence
Evolution of robot brains in fixed bodies
– Social learning, robots teaching robots after deployment (EU DREAM project)
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Evolution of collective behavior
– E.g., drone swarms, hybrid swarms for environment monitoring (Swiss SMAVNET project)
•
Evolutionary design of novel robot bodies
– E.g., soft robots, new forms of actuation and sensing, alternative “brains”
Mid term 5 – 15 years
•
Self-reproducing robots − supervised
– Evolution of robot bodies in real time and real space
– EvoSphere, robot breeding farms (robot foresters, footballers, robot pets)
Long term 15+ years
•
Self-reproducing robots − autonomous
– Robot colonies for terraforming, ultra-deep mining
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Artificial Life
Concerns, issues
• Adaptation, incl. evolution on-the-fly
– How to moderate autonomy without curtailing the system?
– How to validate, certify, guarantee system behavior?
• Evolution “in the wild”
– Need kill switch to prevent runaway evolution
• Security
– Hacker prevention?
• Ethics and moral
– Machine intelligence = alien intelligence?
– Can we make robots ethical?
– If yes, will they deserve rights? (Can we still pull the plug?)
Take home messages
1. Artificial evolution is proven technology
– See Eiben and Smith, Nature 521, May 2015
2. Robot intelligence can be evolved
– Brains within fixed bodies – today
– Bodies & brains together – “next big thing”
3. Evolutionary design/optimization vs. evolutionary adaptation
– Before deployment
– After deployment – changing on-the-fly
4. Simulations help but matter matters
– Reality gap
Biosphere
Robosphere