Modern Robots: Evolutionary Robotics

Transcription

1 Modern Robots: Evolutionary Robotics Jeff Clune Assistant Professor Evolving Artificial Intelligence Laboratory

2 News Congratulations to Roby, Joost, Henok, and Arash!

3 Genotypic vs. Phenotypic (vs. Behavioral) Diversity Use both? Sustaining behavioral diversity in NEAT. Moriguchi & Honiden. GECCO 2010.

4 Your turn to provide feedback Anonymous I want to earn your vote for best class ever please let me know how to do so

5 Homeworks What do you think? Fun to evolve your own critter? Over 50% have over 90% average, many 100%s But now things change!!!!!!!!!

6 Midterm Presentations: General Comments Pro tips: don t talk to the screen more pictures/figures don t over-invest in the first half of your talk In general you should probably avoid writing complete sentences in slides, because no one has the time to read them frogs are born of unicorns while they are listening to you speak, meaning that your content gets lost in an unread paragraph like this one.

7 Midterm Presentations: General Comments Pro tips: don t talk to the screen more pictures/figures don t over-invest in the first half of your talk In general you should probably avoid writing complete sentences in slides, because no one has the time to read them frogs are born of unicorns while they are listening to you speak, meaning that your content gets lost in an unread paragraph like this one. NO complete sentences

8 Status Reports Weekly status report due each Sunday (5am Monday) what you have done since last status report - things you have told me in person should still be included Worth 25% of final project grade first due April 10th mark your calendars on you to remember to turn it in

9 Projects Game time! Less than one month to go! It s harder than you think. Serious progress every 2-3 days Expectations are high for status reports

10 Retina Problem Global Performance: best overall org per run/best ever found by any treatment Global Reliability: per run, averaged over all cells, best found/best ever found by any treatment Precision: GR, but does not include a 0 in a cell if that run did not fill that cell Coverage: Percent of cells filled that are possible to fill (determined by if filled by any treatmt/run)

11 Soft Robot Problem Classic EA Classic EA + Diversity MAP-Elites

12 Soft Robot Problem

13 triped triped triped bipeds Same orgs, from the side % bone fitness % voxels filled jumper two-arm crawler biped biped biped

14 3-legged triped (muscle legs) 3-legged triped (muscle legs) % bone fitness % voxels filled

15 Soft Robot Problem For each arrow, the color of the tip is the fitness of the point, the color of the body is the fitness of the parent Most arrows are short i.e. it s good to search from nearby points that s why MAP-Elites works

16 Lineages of a Few Final Solutions The color of the arrow is the fitness, the dashed lines are different for each final point. Circles are sources (generation 0), their color is irrelevant (random). 4 lineages are shown per panel.

17 Physical, Soft Robot Soft robot arm No closed form solutions for moving tip of arm to specific point i.e inverse kinematics won t work goal: reach every x point as high as possible

18 Physical, Soft Robot Blue: MAP-Elite Red : random sampling Green: exhaustive search

19 Soft robot arm

20 Multi-Objective EAs For multi-objective problems Objectives must be in conflict otherwise solve each one serially

21 Multi-Objective EAs Instead of one solution, we search for the pareto optimal set pareto front - Pareto a solution to an MOP is Pareto optimal if there exists no other feasible solution which would decrease some criterion without causing a simultaneous increase in at least one other criterion. - -Coello Coello 2006 Orgs on pareto front are non-dominated

22 Multi-Objective EAs EAs are one of the best ways to solve multi-objective problems Traditional mathematical solvers may not work if pareto front is concave or disconnected require differentiability of objective functions and constraints mostly generate one solution per run EAs handle all these situations better

23 Multi-Objective EAs: early approaches founder s credit: David Schaffer, mid-80s, VEGA algorithm others do a linear-combination into fitness fitness= objectiveone*.6+objectivetwo*.4 Goldberg 1989 suggests searching for Pareto front by rewarding non-dominated organisms

24 Multi-Objective EAs: basic idea peel off layers one rank at a time fitness = functionof(rank) encourage diversity along front

25 Multi-Objective EAs: takeaway it s better than a linear combination linear combination is just one point on front requires humans to decide tradeoffs may limit search space often decision-makers want options

26 NSGA-II One of the most popular Tie-breaker within rank: prefer less crowded areas Clune, Mouret, & Lipson Proceedings of the Royal Society

27 NSGA-II individual x dominates individual y if both are true: 1. x is not worse than y with respect to any objective 2. x is strictly better than y with respect to at least one objective it s fast & good