Co-evolution of Morphology and Control for Roombots
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1 Co-evolution of Morphology and Control for Roombots Master Thesis Presentation Ebru Aydın Advisors: Prof. Auke Jan Ijspeert Rico Möckel Jesse van den Kieboom Soha Pouya Alexander Spröwitz
2 Co-evolution General Approach : Hardware, Software Body - brain evolution Building Blocks sticks, actuated joints, artificial neurons Encodings direct, direct recurrent, cellular, generative
3 Developmental (Generative) Encoding The algorithm to build the robot Biologically inspired Reuse of components Self-similar structures Compact representation L-system grammars, tree based
4 Previous work: Karl Sims Sims 1994, Evolving 3D Morphology and Behavior (a) Competing (b) Swimming (c) Phenotype and genotype Neural network for controller Tree based encoding for morphology, self similar structures, code reuse A variety of locomotion styles and behavior strategies are evolved
5 Previous work: Demo Lab Projects from Demo Lab : Hornby, Pollack, Lipson Generative Representations for Design Automation Static structures, 2D and 3D robots are evolved. The Golem Project
6 Previous work: Demo Lab Generative encoding builds creatures with better locomotion ability then non-generative encoding Generative encoding brings: Scalability through self-similar structures Hierarchical structure Compact representation
7 Modular robots (k) MTRAN (l) Superbot (m) YAMOR Detach-attach Self-configuration Adaptation
8 Introduction Evolving Robots Roombots Self-reconfigurable Self sufficient Uniform 220mm 110mm 110mm, 1.4kg ACM 3 DOF - Continious rotation Experiments
9 Roombots Self-reconfigurable Self sufficient Uniform 220mm 110mm 110mm, 1.4kg ACM 3 DOF - Continious rotation
10 Co-evolution for the Roombots Modular robots can assemble into many different structures Developmental encoding : Self-similarity, compactness, modularity Co-evolution for Roombots Morphological Building Blocks: Roombots and Passive elements Controller : Coupled Phase Oscillators Encoding (L-systems), combine morphology and controller
11 L-systems Re-write systems Growth process of the organisms Formal grammars G = (V, w, P ) w V + P V V
12 Examples V = {a, b} w = a P = a ab, b a Starting from axiom, a, the following sequence is generated. Step0 : a Step1 : ab Step2 : aba Step3 : abaab
13 Examples Turtle Rules V = {F, +, } w = F F F P = F F + F F + F
14 Examples Branching L-systems [ ] V = {F, +,, [, ]} w = F P = F F [+F ]F [ F [+F ][ F ]]F
15 L-system for the Roombots Deterministic branching L-systems An L-system a robot Additive turtle like interpretation Constants Build commands Build commands : 1. Add a new part 2. Modify an open parameter
16 Controller CPG, rhythmic patterned output Limit cycle behavior robust Parameterize S1 S1 servo motor oscillator M1 M1 S2 S2
17 Controller φ i = 2 π ν + ω ij r j sin (φ j φ i ψ ij ) ṙ i = a(r i r i ) ẋ i = b(x i x i ) Fixed parameters : ν, ω ij, a, b Open parameters : R i, ψ ij, X i
18 Controller φ i = 2 π ν + ω ij r j sin (φ j φ i ψ ij ) ṙ i = a(r i r i ) ẋ i = b(x i x i ) Fixed parameters : ν, ω ij, a, b Open parameters : R i, ψ ij, X i Drive functions: θ i = r i sin (φ i ) + x i θ i = ±φ i θ i = x i Oscillation Rotation Locked
19 Morphology Roombots modules Connection face Connection type Phase offset Passive elements, spring-damper systems Connection face Length
20 State State : Module, Connection face, Phase bias State update commands: ADD CONNECTION TYPE, CONNECTION FACE CHANGE FACE N CHANGE PHASE BIAS BM N
21 State State : Module, Connection face, Phase bias State update commands: ADD CONNECTION TYPE, CONNECTION FACE CHANGE FACE N CHANGE PHASE BIAS BM N CHANGE VALUE N command is interpreted as: VALUE VALUE + N (mod S) ACT IV E F ACE ACT IV E F ACE + N (mod4)
22 Build Commands: BRANCH OPERATOR : [ ] REPETITION OPERATOR : { } N ADD CONNECTION TYPE, CONNECTION FACE ADD PASSIVE LEG: LENGTH Modify Control Parameters: CHANGE AMPLITUDE OSC N CHANGE OFFSET OSC N CHANGE DRIVE FUNCTION OSC N CHANGE PHASE BIAS COUPLING N
23 L-system Grammars Deterministic, context-free, branching Four rules Non-recursive The grammar encodes the build process of the robot Table: Build Commands Abbreviations Command Abbreviation ADD ADD CHANGE FACE CF CHANGE PHASE BIAS BM CPBBM CHANGE PHASE BIAS CPB CHANGE AMPLITUDE CA CHANGE OFFSET CO CHANGE DRIVE FUNCTION CDF
24 An example grammar R1 CPB(s1::m1 90) CA(m1 1.4) CO(s2 150) CPB(m1::s2 220) CO(m1 290) R2 ADD(C1Y PER) CDF(s1 3) CDF(s2 3) R1 CA(s2 1.4) R3 CPB(s1::m1 100) R2 CA(m1 2.5) 1 x { CA(m1 2.4) } R4 CDF(m1 2) CO(s2 340) CPBBM(100) R2 R2 R3 CF(2)
25 step0 : R4 Rewrite steps
26 Rewrite steps step0 : R4 step1 : CDF(m1 2) CO(s2 340) CPBBM(100) R2 R2 R3 CF(2)
27 Rewrite steps step0 : R4 step1 : CDF(m1 2) CO(s2 340) CPBBM(100) R2 R2 R3 CF(2) step2 : CDF(m1 2) CO(s2 340) CPBBM(100) ADD(C1Y PER) CDF(s1 3) CDF(s2 3) R1 CA(s2 1.4) ADD(C1Y PER) CDF(s1 3) CDF(s2 3) R1 CA(s2 1.4) CPB(s1::m1 100) R2 CA(m1 2.5) 1 x { CA(m1 2.4) } CF(2)
28 Rewrite steps step0 : R4 step1 : CDF(m1 2) CO(s2 340) CPBBM(100) R2 R2 R3 CF(2) step2 : CDF(m1 2) CO(s2 340) CPBBM(100) ADD(C1Y PER) CDF(s1 3) CDF(s2 3) R1 CA(s2 1.4) ADD(C1Y PER) CDF(s1 3) CDF(s2 3) R1 CA(s2 1.4) CPB(s1::m1 100) R2 CA(m1 2.5) 1 x { CA(m1 2.4) } CF(2) step3 : CDF(m1 2) CO(s2 340) CPBBM(100) ADD(C1Y PER) CDF(s1 3) CDF(s2 3) CPB(s1::m1 90) CA(m1 1.4) CO(s2 150) CPB(m1::s2 220) CO(m1 290) CA(s2 1.4) ADD(C1Y PER) CDF(s1 3) CDF(s2 3) CPB(s1::m1 90) CA(m1 1.4) CO(s2 150) CPB(m1::s2 220) CO(m1 290) CA(s2 1.4) CPB(s1::m1 100) ADD(C1Y PER) CDF(s1 3) CDF(s2 3) R1 CA(s2 1.4) CA(m1 2.5) 1 x { CA(m1 2.4) } CF(2)
29 Rewrite Steps step4 : CDF(m1 2) CO(s2 340) CPBBM(100) ADD(C1Y PER) CDF(s1 3) CDF(s2 3) CPB(s1::m1 90) CA(m1 1.4) CO(s2 150) CPB(m1::s2 220) CO(m1 290) CA(s2 1.4) ADD(C1Y PER) CDF(s1 3) CDF(s2 3) CPB(s1::m1 90) CA(m1 1.4) CO(s2 150) CPB(m1::s2 220) CO(m1 290) CA(s2 1.4) CPB(s1::m1 100) ADD(C1Y PER) CDF(s1 3) CDF(s2 3) CPB(s1::m1 90) CA(m1 1.4) CO(s2 150) CPB(m1::s2 220) CO(m1 290) CA(s2 1.4) CA(m1 2.5) 1 x { CA(m1 2.4) } CF(2)
30 Interpretation of the string CDF(m1 2) CO(s2 340) CPBBM(100)... CDF(m1 2) Rotation ( φ i ) CO(s2 340) CPBBM(100) Update state
31 Interpretation of the string... ADD(C1Y PER) CDF(s1 3) CDF(s2 3) CPB(s1::m1 90) CA(m1 1.4) CO(s2 150) CPB(m1::s2 220) CO(m1 290) CA(s2 1.4)...
32 Interpretation of the string... ADD(C1Y PER) CDF(s1 3) CDF(s2 3) CPB(s1::m1 90) CA(m1 1.4) CO(s2 150) CPB(m1::s2 220) CO(m1 290) CA(s2 1.4) CPB(s1::m1 100)...
33 Interpretation of the string... ADD(C1Y PER) CDF(s1 3) CDF(s2 3) CPB(s1::m1 90) CA(m1 1.4) CO(s2 150) CPB(m1::s2 220) CO(m1 290) CA(s2 1.4) CA(m1 2.5) CA(m1 2.4)
34 Evolutionary Algorithm The L-system grammars are evolved. a- Mutation Change parameter of a build command Add rule to rewrite rule Remove rule from rewrite rule b- Crossover : Use an index, copy the rules from parents: r1 r2 r3 r4
35 Evolutionary Algorithm c- Selection : Roulette wheel fitness = Evaluation in simulation Straight line motion Positive change of the impact, safe Energy efficiency speed a impact b torque c
36 Implementation Optimizer Grammar World builder cpg.xml Parser world.xml World maker Fitness Simulator world.wbt
37 Experiments Two sets of experiments Roombots Roombots and passive legs Generation limit 500 th Modules : 2 to 8 Deterministic simulation environment Distributed over 40 computers 3 to 5 hours
38 An example Run
39 An example Run Best fitness changed at 7 points between 20 th and 50 th generations speed (m/s) impact x 10 4 torque (Nm) 7 6 fitness
40 An example Run speed (m/s) impact x 10 4 torque (Nm) 7 6 fitness rd - Mutation (parameter modification), best of the previous generation
41 An example Run speed (m/s) impact x 10 4 torque (Nm) 7 6 fitness th - Crossover of two average individual
42 An example Run speed (m/s) impact x 10 4 torque (Nm) 7 6 fitness th - Crossover, same morphologies but different controllers
43 Evaluation of the results Many different locomotion strategies are evolved Videos...
44 Roombots and Passive Elements Experiments Two types Between modules (2 connection faces) Attached to one module (1 connection face) (*) Telescopic motion Spring-damper systems Modules : 2 to 4 Passive Legs : 2 to 6 The aim is to explore effects of the passive compliant elements on the fitness criteria Videos...
45 Figure: The average speeds and fitness values according to number of modules of the robots. Comparison of the results (p) (q)
46 Figure: The average impact and torque values according to number of modules of the robots. Comparison of the results (a) (b)
47 Passive Elements Between Modules Rotational and linear Unstable behavior Unrealistic speeds (up to 2m/s) Robots disconnected Fast turn, gyroscope threshold did not work Reason could not be found ACM very stiff, each module is a robot A better model is needed
48 Conclusion Developmental process to evolve the morphology and controllers of the modular robots L-system grammars encoded the build process are evolved A variety of robots with different locomotion strategies are evolved The passive legs enable faster gaits by exploiting the energy stored at the legs The system can be used to test other types of passive elements The fitness criteria can be adapted for other tasks
49 Future Work Sensory feedback Other tasks Unstable passive elements
50 Thank you for listening! Questions...
51 6% 6% 13% 2 modules 3 modules 4 modules 5 modules 7 modules 19% 56% Evaluation of the results Robot Count Number of Modules Distribution of the results according to the number of modules of the fittest robot. The robot counts according to their modules in the initial population. Each generation is composed of 100 individuals
52 0.45 Evaluation of the results 3.5 x Average speed (m/s) Average Fitness Number of Modules Number of Modules Figure: These results are grouped by the number of the modules of the robot.
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