17 research outputs found
A Minimal Developmental Model Can Increase Evolvability in Soft Robots
Different subsystems of organisms adapt over many time scales, such as rapid
changes in the nervous system (learning), slower morphological and neurological
change over the lifetime of the organism (postnatal development), and change
over many generations (evolution). Much work has focused on instantiating
learning or evolution in robots, but relatively little on development. Although
many theories have been forwarded as to how development can aid evolution, it
is difficult to isolate each such proposed mechanism. Thus, here we introduce a
minimal yet embodied model of development: the body of the robot changes over
its lifetime, yet growth is not influenced by the environment. We show that
even this simple developmental model confers evolvability because it allows
evolution to sweep over a larger range of body plans than an equivalent
non-developmental system, and subsequent heterochronic mutations 'lock in' this
body plan in more morphologically-static descendants. Future work will involve
gradually complexifying the developmental model to determine when and how such
added complexity increases evolvability
Evolving Spatially Aggregated Features for Regional Modeling and its Application to Satellite Imagery
Satellite imagery and remote sensing provide explanatory variables at relatively high resolutions for modeling geospatial phenomena, yet regional summaries are often desirable for analysis and actionable insight. In this paper, we propose a novel method of inducing spatial aggregations as a component of the statistical learning process, yielding regional model features whose construction is driven by model prediction performance rather than prior assumptions. Our results demonstrate that Genetic Programming is particularly well suited to this type of feature construction because it can automatically synthesize appropriate aggregations, as well as better incorporate them into predictive models compared to other regression methods we tested. In our experiments we consider a specific problem instance and real-world dataset relevant to predicting snow properties in high-mountain Asia
Design for an Increasingly Protean Machine
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
Reinforcement learning for freeform robot design
Inspired by the necessity of morphological adaptation in animals, a growing
body of work has attempted to expand robot training to encompass physical
aspects of a robot's design. However, reinforcement learning methods capable of
optimizing the 3D morphology of a robot have been restricted to reorienting or
resizing the limbs of a predetermined and static topological genus. Here we
show policy gradients for designing freeform robots with arbitrary external and
internal structure. This is achieved through actions that deposit or remove
bundles of atomic building blocks to form higher-level nonparametric
macrostructures such as appendages, organs and cavities. Although results are
provided for open loop control only, we discuss how this method could be
adapted for closed loop control and sim2real transfer to physical machines in
future
Efficient automatic design of robots
Robots are notoriously difficult to design because of complex
interdependencies between their physical structure, sensory and motor layouts,
and behavior. Despite this, almost every detail of every robot built to date
has been manually determined by a human designer after several months or years
of iterative ideation, prototyping, and testing. Inspired by evolutionary
design in nature, the automated design of robots using evolutionary algorithms
has been attempted for two decades, but it too remains inefficient: days of
supercomputing are required to design robots in simulation that, when
manufactured, exhibit desired behavior. Here we show for the first time de-novo
optimization of a robot's structure to exhibit a desired behavior, within
seconds on a single consumer-grade computer, and the manufactured robot's
retention of that behavior. Unlike other gradient-based robot design methods,
this algorithm does not presuppose any particular anatomical form; starting
instead from a randomly-generated apodous body plan, it consistently discovers
legged locomotion, the most efficient known form of terrestrial movement. If
combined with automated fabrication and scaled up to more challenging tasks,
this advance promises near instantaneous design, manufacture, and deployment of
unique and useful machines for medical, environmental, vehicular, and
space-based tasks