1. Soft Robotics Evolve the body and brain of `soft’ robots. N Cheney, R MacCurdy, J Clune, H Lipson. Unshackling Evolution: Evolving Soft Robots with Multiple Materials and Powerful Generative Encoding. GECCO 2013. Robots are composed of a set of voxels, rather than a collection of rigid 3D shapes. Some of the voxels are hard or soft; some of the voxels are `muscles’: they change in volume. HyperNEAT is used to evolve the bodies. The robots have no brain: their ‘muscles’ pulse at a regular frequency. ‘muscle’ type 1: change volume ‘fat’: soft, deformed by neighboring voxels muscle type 2: change volume in antiphase to mt1 bone: rigid, is not deformed by neighboring voxels Voxel = three-dimensional pixel

2. Soft Robotics Can create more complex robots than `rigid’ ones N Cheney, R MacCurdy, J Clune, H Lipson. Unshackling Evolution: Evolving Soft Robots with Multiple Materials and Powerful Generative Encoding. GECCO 2013. Why little progress in 19 years? Evolution has few options if asked to put together a few shapes; Much more design freedom for large number of voxels with different material properties

3. Soft Robotics Why use HyperNEAT? N Cheney, R MacCurdy, J Clune, H Lipson. Unshackling Evolution: Evolving Soft Robots with Multiple Materials and Powerful Generative Encoding. GECCO 2013. Robots are composed of a set of voxels, rather than a collection of rigid 3D shapes. Voxels have different material properties (e.g. hard/soft) Would like to have regular 3D patterns of different kinds of voxels throughout the robot. X Y Greyscale of pixel xy X Y Z Deposit plastic droplet (y/n)

4. Soft Robotics Evolve the body and brain of `soft’ robots. N Cheney, R MacCurdy, J Clune, H Lipson. Unshackling Evolution: Evolving Soft Robots with Multiple Materials and Powerful Generative Encoding. GECCO 2013. Take a CPPN. Feed in the x, y, z position of each pixel within a cube. Also d, its position from the cube’s center. One binary output value: pixel present/absent four continuous output values: pixel type 1, … pixel type 4 If pixel present, place pixel type with maximum value. If multiple patches, take the one closest to the center. max( )

5. Soft Robotics Physical soft robots. N Cheney, R MacCurdy, J Clune, H Lipson. Unshackling Evolution: Evolving Soft Robots with Multiple Materials and Powerful Generative Encoding. GECCO 2013. Physical robot is composed of two voxel types: red and white. Robot placed in a pressure chamber. Q: How does the robot move?

6. Soft Robotics Soft robots of variable resolution. N Cheney, R MacCurdy, J Clune, H Lipson. Unshackling Evolution: Evolving Soft Robots with Multiple Materials and Powerful Generative Encoding. GECCO 2013. Genotype = CPPN (Compositional Pattern-Producing Network) Can produce multiple phenotypes (robots) at different resolutions. Simply use smaller voxels and requery the CPPN. Re-query CPPN at higher resolutio n (below is from lecture 23)

7. Soft Robotics Compare HyperNEAT to a direct encoding N Cheney, R MacCurdy, J Clune, H Lipson. Unshackling Evolution: Evolving Soft Robots with Multiple Materials and Powerful Generative Encoding. GECCO 2013. Q: How would you define a genotype to directly encode a 10x10x10=10 3 voxel soft robot composed of four different voxel types? How many `genes’ would there be?

8. Soft Robotics Evolve the body and brain of `soft’ robots. N Cheney, R MacCurdy, J Clune, H Lipson. Unshackling Evolution: Evolving Soft Robots with Multiple Materials and Powerful Generative Encoding. GECCO 2013. Generative encoding produces regular patterns of the same voxel type. “In the direct encoding, each voxel works independently from—and often at odds with—its neighboring voxels, preventing coordinated behaviors.” (section 4.1)

9. Soft Robotics N Cheney, R MacCurdy, J Clune, H Lipson. Unshackling Evolution: Evolving Soft Robots with Multiple Materials and Powerful Generative Encoding. GECCO 2013. FitnessFn1: displacement (d) Fitness functions 2 through 4: add a penalty term of the form F = d * ( 1 - penalty/maxPenalty ) FF2: d * (1–usedVoxels/1000) FF3: d * (1 – connsBetVoxels/ maxConnsBetVoxels ) FF4: d * (1–usedActVoxels/1000 ) How different fitness functions affect evolution.

10. Soft Robotics How different fitness functions affect the robot phenotypes. N Cheney, R MacCurdy, J Clune, H Lipson. Unshackling Evolution: Evolving Soft Robots with Multiple Materials and Powerful Generative Encoding. GECCO 2013. FitnessFn1: displacement (d) Fitness functions 2 through 4: add a penalty term of the form F = d * ( 1 - penalty/maxPenalty ) FF2: d * (1–usedVoxels/1000) FF3: d * (1 – connsBetVoxels/ maxConnsBetVoxels ) FF4: d * (1–usedActVoxels/1000 )

11. Soft Robotics

12. Evolution of biologically-similar phenotypes. N Cheney, R MacCurdy, J Clune, H Lipson. Unshackling Evolution: Evolving Soft Robots with Multiple Materials and Powerful Generative Encoding. GECCO 2013.

13. Soft Robotics Do the different voxel types matter? N Cheney, R MacCurdy, J Clune, H Lipson. Unshackling Evolution: Evolving Soft Robots with Multiple Materials and Powerful Generative Encoding. GECCO 2013. 35 evolutionary runs… …with all four voxel types. 35 evolutionary runs… …with red, blue and green voxels. 35 evolutionary runs… …with just red and blue voxels. 35 evolutionary runs… …with just red voxels. ‘muscle’ type 1: change volume ‘fat’: soft, deformed by neighboring voxels muscle type 2: change volume in antiphase to mt1 bone: rigid, is not deformed by neighboring voxels

14. Soft Robotics Getting beyond locomotion over flat ground. N Cheney, R MacCurdy, J Clune, H Lipson. Unshackling Evolution: Evolving Soft Robots with Multiple Materials and Powerful Generative Encoding. GECCO 2013. How to get to object manipulation? Start by ‘grabbing’ obstacles on the ground and pushing or pulling yourself forward..