Evolving Robot Intelligence
Transcription
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 – – – – 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) • 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 – 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