- author = "Sam Kriegman",
- title = "Design for an Increasingly Protean Machine",
- school = "Computer Science, University of Vermont",
- year = "2020",
- address = "USA",
- month = oct,
- keywords = "genetic algorithms, genetic programming, Evo-Devo",
-
URL = "
https://scholarworks.uvm.edu/graddis/1330/",
-
URL = "https://scholarworks.uvm.edu/cgi/viewcontent.cgi?article=2331&context=graddis",
- size = "211 pages",
-
abstract = "Data-driven, rather than hypothesis-driven, approaches
to robot design are becoming increasingly widespread,
but they remain narrowly focused on tuning the
parameters of control software (neural network synaptic
weights) inside an overwhelmingly static and
presupposed body. Meanwhile, an efflorescence of new
actuators and metamaterials continue to broaden the
ways in which machines are free to move and morph, but
they have yet to be adopted by useful robots because
the design and control of metamorphosing body plans is
extremely non-intuitive. This thesis unites these
converging yet previously segregated technologies by
automating the design of robots with physically
malleable hardware, which we will refer to as protean
machines, named after Proteus of Greek mythology.
This thesis begins by proposing an ontology of embodied agents, their physical features, and their potential ability to purposefully change each one in space and time. A series of experiments are then documented in which increasingly more of these features (structure, shape, and material properties) were allowed to vary across increasingly more timescales (evolution, development, and physiology), and collectively optimized to facilitate adaptive behavior in a simulated physical environment. The utility of increasingly protean machines is demonstrated by a concomitant increase in both the performance and robustness of the final, optimized system. This holds true even if its ability to change is temporarily removed by fabricating the system in reality, or by canalization: the tendency for plasticity to be supplanted by good static traits (an inductive bias) for the current environment. Further, if physical flexibility is retained rather than canalized, it is shown how protean machines can, under certain conditions, achieve a form of hyper-robustness: the ability to self-edit their own anatomy to undo large deviations from the environments in which their control policy was originally optimized.
Some of the designs that evolved in simulation were manufactured in reality using hundreds of highly deformable silicone building blocks, yielding shapeshifting robots. Others were built entirely out of biological tissues, derived from pluripotent Xenopus laevis stem cells, yielding computer-designed organisms (dubbed xenobots). Overall, the results shed unique light on questions about the evolution of development, simulation-to-reality transfer of physical artifacts, and the capacity for bioengineering new organisms with useful functions.",
-
notes = "'Design for an Increasingly Protean
Machine.pdf'
Supervisor: Josh Bongard",
