Presentation of conference publication at the 2019 IEEE Congress on Evolutionary Computation.

1. www.data61.csiro.au
Comparing Direct and Indirect
Representations for Environment-Specific
Robot Component Design
Jack Collins1,2, Ben Cottier1,3, David Howard1
1 Data61/CSIRO, Brisbane, Australia
2 Queensland University of Technology (QUT), Brisbane, Australia
3 University of Queensland (UQ), Brisbane, Australia

2. Background
Comparing Direct and Indirect Representations for Environment-Specific Robot Component Design | Jack Collins2 |
• In past research we evolved the morphology of
hexapod legs for specific environments
• J. Collins, W. Geles, D. Howard and F. Maire. “Towards the
targeted environment-specific evolution of robot components”
GECCO 2018.
• This approach was unique in that we:
• Used targeted evolution to evolve static (unactuated) segments
of a robot
• Instantiated evolved components (3D Printed)
• Direct Representation to map genotype to phenotype
• Utilised a mature control stack and platform
Human Designed Legs
3D Printed Evolved Legs

3. Motivation
Comparing Direct and Indirect Representations for Environment-Specific Robot Component Design | Jack Collins3 |
• Our direct representation was restrictive:
• Contained Superfluous material
• Solutions were far from optimal for the fitness heuristic
• We wanted to explore other representations that
would afford:
• Biologically mimicking solutions
• Greater exploration of the design space
• Improved fitness
Direct Representation Indirect Representation

4. Design Space
Comparing Direct and Indirect Representations for Environment-Specific Robot Component Design | Jack Collins4 |
0 = Empty
1 = Full
A voxel
Design Space: 16x16X32 (8192) Voxels
Mounting Bracket

5. Evaluation Environment
Comparing Direct and Indirect Representations for Environment-Specific Robot Component Design | Jack Collins5 |
• Project Chrono Simulator
• High Fidelity Simulator
• https://projectchrono.org/
• Environments
• Deformable soil terrain
• DEM Gravel
• DEM Fluid

6. Fitness
Comparing Direct and Indirect Representations for Environment-Specific Robot Component Design | Jack Collins6 |
• We use a heuristic, it includes two important
parameters
- Accumulated Torque
• Reducing torque increases the efficiency of a “step”
• Increases attainable mission times
- Occupied Voxels
• Minimises unnecessary material, reducing printing times and
cost per leg.
• A higher fitness is the objective
𝑓 =
1
𝜏
𝑛 𝑠𝑡𝑒𝑝𝑠
+
𝜏
𝑛 𝑠𝑡𝑒𝑝𝑠
×
𝛿
5
Where:
τ is Torque(Nm)
nsteps is the number of simulation steps
Δ is the percentage of occupied voxels

7. Direct Representation
Comparing Direct and Indirect Representations for Environment-Specific Robot Component Design | Jack Collins7 |
• Bezier Splines
- Each Spline
• 3-8 Control Points
• Must originate at top plane
and finish at bottom plane
- Each Leg
• 5-10 Bezier
• Bezier Thickness 1-3 Voxels
• Genetic Algorithm
- Population of 20
- 50 Generations
- Tournament Selection
• 4 Candidates

8. Indirect Representation
Comparing Direct and Indirect Representations for Environment-Specific Robot Component Design | Jack Collins8 |
• Compositional Pattern Producing Networks
- Input: x,y,z position of each voxel
- Output: Float between 0 and 1
- Activation Functions: Sine, Cosine, Identity, Guassian,
Absolute, Sigmoid
- Activation Functions promote properties within the evolved
structure
• CPPN-NEAT
- Evolves network structure, weight and activation function
- Evolves increasingly complex networks

9. Compliance Method
Comparing Direct and Indirect Representations for Environment-Specific Robot Component Design | Jack Collins9 |
Thresholding
• Threshold begins at 0.5
• Progressively lowered until a valid leg is generated

10. Compliance Method
Comparing Direct and Indirect Representations for Environment-Specific Robot Component Design | Jack Collins9 |
Thresholding Scaling
• Check for Compliance, Find Largest Artefact,
Compute Scaling Ratio, Rescale
• Similar to Auerbach & Bongard (2014)

11. Experiment 1
Comparing Direct and Indirect Representations for Environment-Specific Robot Component Design | Jack Collins10 |
• Compare the two compliance methods
• Thresholding and Scaling had similar performance
in all environments
• Results aren’t statistically significant
Environment Method Best
Average
(10 Runs)
Soil
Threshold 29.0705 20.1488
Scaling 29.0593 23.1240
Gravel
Threshold 0.1294 0.1239
Scaling 0.1582 0.1243
Fluid
Threshold 0.1560 0.1380
Scaling 0.1560 0.1433

12. Experiment 1
Comparing Direct and Indirect Representations for Environment-Specific Robot Component Design | Jack Collins10 |
• Compare the two compliance methods
• Thresholding and Scaling had similar performance
in all environments
• Results aren’t statistically significant
Environment Method Best
Average
(10 Runs)
Soil
Threshold 29.0705 20.1488
Scaling 29.0593 23.1240
Gravel
Threshold 0.1294 0.1239
Scaling 0.1582 0.1243
Fluid
Threshold 0.1560 0.1380
Scaling 0.1560 0.1433

13. Experiment 1
Comparing Direct and Indirect Representations for Environment-Specific Robot Component Design | Jack Collins10 |
• Compare the two compliance methods
• Thresholding and Scaling had similar performance
in all environments
• Results aren’t statistically significant
Environment Method Best
Average
(10 Runs)
Soil
Threshold 29.0705 20.1488
Scaling 29.0593 23.1240
Gravel
Threshold 0.1294 0.1239
Scaling 0.1582 0.1243
Fluid
Threshold 0.1560 0.1380
Scaling 0.1560 0.1433

14. Experiment 1
Comparing Direct and Indirect Representations for Environment-Specific Robot Component Design | Jack Collins11 |
• Gravel and Fluid are distinctly different
to Soil
• Little increase in fitness after ≈10 generations
• Much lower fitness values
• Direct result of the DEM environment requiring
higher torques
• Gravel is an unchallenging environment
• STD of best and average lines are low
• Plateau of fitness

15. Experiment 2
Comparing Direct and Indirect Representations for Environment-Specific Robot Component Design | Jack Collins12 |
• Comparing direct and indirect
representations
• Experiments were statistically significant
using the Mann-Whitney U-Test (P=0.0257,
0.0028, 0.0002)

16. Experiment 2
Comparing Direct and Indirect Representations for Environment-Specific Robot Component Design | Jack Collins12 |
• Comparing direct and indirect
representations
• Experiments were statistically significant
using the Mann-Whitney U-Test (P=0.0257,
0.0028, 0.0002)
Environment Representation Best
Soil
Bezier 11.0619
CPPN 29.0593

17. Experiment 2
Comparing Direct and Indirect Representations for Environment-Specific Robot Component Design | Jack Collins12 |
• Comparing direct and indirect
representations
• Experiments were statistically significant
using the Mann-Whitney U-Test (P=0.0257,
0.0028, 0.0002)
Environment Representation Best
Soil
Bezier 11.0619
CPPN 29.0593
Gravel
Bezier 0.1237
CPPN 0.1479

18. Experiment 2
Comparing Direct and Indirect Representations for Environment-Specific Robot Component Design | Jack Collins12 |
• Comparing direct and indirect
representations
• Experiments were statistically significant
using the Mann-Whitney U-Test (P=0.0257,
0.0028, 0.0002)
Environment Representation Best
Soil
Bezier 11.0619
CPPN 29.0593
Gravel
Bezier 0.1237
CPPN 0.1479
Fluid
Bezier 0.0748
CPPN 0.1404

19. Experiment 2
Comparing Direct and Indirect Representations for Environment-Specific Robot Component Design | Jack Collins19 |
• Comparing direct and indirect
representations
• Experiments were statistically significant
using the Mann-Whitney U-Test (P=0.0257,
0.0028, 0.0002)
• CPPN has a greater Standard Deviation,
inherent nature of indirect encodings (small
changes to genotype = larger changes to
phenotype)
• In our context an indirect representation is
better
Environment Representation Best
Soil
Bezier 11.0619
CPPN 29.0593
Gravel
Bezier 0.1237
CPPN 0.1479
Fluid
Bezier 0.0748
CPPN 0.1404

20. Comparing Direct and Indirect Representations for Environment-Specific Robot Component Design | Jack Collins20 |
Morphology
• Soil legs show the effect the second term in the
fitness function has on reducing material
• Gravel legs have a large foot tip and a
streamlined shape, displaying the complexity
that CPPN-NEAT can elicit
• Multiple Fluid legs reached a common best
fitness value. Evolution found two approaches,
a commonly hydro-dynamic design and large,
flat faces perpendicular to motion

21. Contributions and Future Work
Comparing Direct and Indirect Representations for Environment-Specific Robot Component Design | Jack Collins21 |
• We compare a direct and indirect
representation in the context of
environment-specific leg design
• We also investigate constraint satisfaction for
CPPN-NEAT finding that both a thresholding and
scaling approaches produce similar results.
• FEA (Finite Element Analysis)
• Structural Integrity of designs
• Real-world Validation
• Validate our simulated designs

22. www.data61.csiro.au
Comparing Direct and Indirect
Representations for Environment-Specific
Robot Component Design
Jack Collins1,2, Ben Cottier1,3, David Howard1
1 Data61/CSIRO, Brisbane, Australia
2 Queensland University of Technology (QUT), Brisbane, Australia
3 University of Queensland (UQ), Brisbane, Australia