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

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.

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.

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.

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

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

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

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

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

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.

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

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

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 = 1000 (verfiy)

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

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

Questions?