1. Andrew Danise Advisor: Prof. John Rieffel
Evolving Soft Robots
Andrew Danise
Advisor: Prof. John Rieffel

2. Why Soft Robots? Rieffel, Trimmer, Lipson, 2008.
1 minute
Soft robots have no rigid structure, so they have more:
- flexibility
- maneuverability     
- deformable shape and stretch
Where could this be useful?
- Squeeze in between rubble of Natural Disasters, search for survivors
- Reach places that rigid body robots would have difficulty reaching
Cons: - complexity of design
Rieffel, Trimmer, Lipson, 2008.
Whitesides et al., 2011.

3. How to Design Soft Robots?
Rieffel et al., 2009.
Rieffel and Smith, 2012.
1:30 minute
To design a soft robot optimized for locomotion:
Factors to consider:
- What shape would be the best for movement?
- What should be the mechanism it uses for movement? (oscillation of the stiffness of materials)
- These two factors influence each other
Lack of any analytical intuition:
- use genetic algorithm : create an initial population of designs
- create each design in a physics engine, and judge the fitness of the design
in this case fitness is how far the design moves
Get rid of the worst performing designs
Generate new designs from the successful designs
- To create new designs in the genetic algorithm you use encodings:
Cheney et al., 2013.

4. Generative Encodings Lohn, Hornby, and Linden, 2004.
Rieffel and Smith, 2012.
1 minute
Ways to create new designs:
- Direct Encoding:
   - example: choose set of randomly placed tetrahedra and test their fitness
   - Drawbacks:
      - design not always viable (tetrahedra disjointed, overlapping)
      - random (difficult to expand on a good design)
      - essentially starting from scratch with each design
- Generative Encoding:
   - start from a single tetrahedra and “expand” it using a set of rules
   - ex: tetrahedral face encoding grammar (explain the grammar in detail, use picture as a guide)
   - Benefits:
      - design is guaranteed to be feasible (if rules crafted well)
      - easy to create widespread coordinated change (symmetry, modularity)
        - ex: Table
- Direct : increase the length of each leg individually
- Generative : increase all the legs at the same time
      
Rieffel and Smith, 2012.

5. Sample Face Encoding Rules
1 minute
Sample set of rules
Discuss the specific rules that can be done (mention the colors are used instead of letters)
Grow
Subdivide
relabel
This slowly expand a soft robot design by applying the rules to the soft robot design, one face at a time

6. 1st Expansion
Applied Rule: Green → subdivide(Green, Blue, Orange, Red)
10 seconds
Explain how the faces are expanded in order that they are added to the soft robot
Rules applied one face at a time
- get the next face
- apply the rule corresponding with that faces color
This is running in the simulation that I made

7. 2nd Expansion
Applied Rule: Blue → grow(Green, Blue, Blue)
10 seconds

8. 3rd Expansion
Applied Rule: Orange → relabel(Blue)
10 seconds

9. After 20 Expansions 30 seconds
What’s the catch? Answer: The Stopping Problem

10. The Stopping Problem Rieffel, 2013. 1 minute
The Main Drawback of generative encodings is the halting problem.
Red line is the final generation.
Black line is the fitness of the each generation
Static Developmental Timings:
 - expand using the generative encoding to a pre-decided number of generations
Sometimes static developmental timing gets the best fitness possible
(Top graphs)
However, other times it does not get the best fitness possible (Bottom Graph)
- would have achieved better fitness if it stop earlier or later
Scaled Developmental Timings:
- expand using generative encoding and slowly rachet up the number of generations
This leads to research question -> next slide
Rieffel, 2013.

11. Research Question
How well can Scaled Developmental Timings help to address the stopping problem?
How to make a soft robot move?
1 minute
Due to the nature of Scaled Developmental Timings (slowly building up) it has many benefits over static developmental timings:
For a given level of fitness achieved with Static and Scaled Developmental Timings:
More computationally efficient (fewer tetrahedra to simulate)
Less complex solutions (fewer tetrahedra in final design)
See if the process of racheting up the number of generations can help address the halting problem
BUT FIRST!!! Need to find a way for the robots to move

12. Using Vibration for Movement
2 minutes
To look at the research question, need a way to move the robots
Already have a way to design the shape of a robot : generative encodings
Look at vibration as a movement mechanism
Similar to research into movement of tensegrity robots
Play video:
Discuss the implementation
How one cylinder rotates around the other
How the other cylinder is attached to the soft robot, always in the same spot
Cylinders don’t collide with anything but can influence the soft robot

13. Implementation 3 minutes
These are all first generation – easier for me to take the video
How to implement the encoding
Currently, the simulation uses only static developmental timings
Stats on runtime:
Graphics ON:
- Population = 6, Generations = 2, ~10 minutes
- Population = 10, Generations = 5, ~43 minutes
Sample Population = 40 (verify)
Sample Generation = (verfiy)

14. Sample Generated Design
1 minute
3D printed robots:
Simulated rendering of generated tetrahedral mesh
Corresponding real 3D printed design
Pager motors could be inserted into the printed design
Work similarly to the simulated vibration
Could be used to produce real soft robots that can move

15. Future Work Evolve and 3D print more designs and make them move
Run simulation on the Union College cluster
Compare Static and Scaled Developmental Timings
1 minute
Explain what parts I have already done
Explain the future work to do

16. Questions?