16 research outputs found
Scalable Co-Optimization of Morphology and Control in Embodied Machines
Evolution sculpts both the body plans and nervous systems of agents together
over time. In contrast, in AI and robotics, a robot's body plan is usually
designed by hand, and control policies are then optimized for that fixed
design. The task of simultaneously co-optimizing the morphology and controller
of an embodied robot has remained a challenge. In psychology, the theory of
embodied cognition posits that behavior arises from a close coupling between
body plan and sensorimotor control, which suggests why co-optimizing these two
subsystems is so difficult: most evolutionary changes to morphology tend to
adversely impact sensorimotor control, leading to an overall decrease in
behavioral performance. Here, we further examine this hypothesis and
demonstrate a technique for "morphological innovation protection", which
temporarily reduces selection pressure on recently morphologically-changed
individuals, thus enabling evolution some time to "readapt" to the new
morphology with subsequent control policy mutations. We show the potential for
this method to avoid local optima and converge to similar highly fit
morphologies across widely varying initial conditions, while sustaining fitness
improvements further into optimization. While this technique is admittedly only
the first of many steps that must be taken to achieve scalable optimization of
embodied machines, we hope that theoretical insight into the cause of
evolutionary stagnation in current methods will help to enable the automation
of robot design and behavioral training -- while simultaneously providing a
testbed to investigate the theory of embodied cognition
Some Experiments on the influence of Problem Hardness in Morphological Development based Learning of Neural Controllers
Natural beings undergo a morphological development process of their bodies
while they are learning and adapting to the environments they face from infancy
to adulthood. In fact, this is the period where the most important learning
pro-cesses, those that will support learning as adults, will take place.
However, in artificial systems, this interaction between morphological
development and learning, and its possible advantages, have seldom been
considered. In this line, this paper seeks to provide some insights into how
morphological development can be harnessed in order to facilitate learning in
em-bodied systems facing tasks or domains that are hard to learn. In
particular, here we will concentrate on whether morphological development can
really provide any advantage when learning complex tasks and whether its
relevance towards learning in-creases as tasks become harder. To this end, we
present the results of some initial experiments on the application of
morpho-logical development to learning to walk in three cases, that of a
quadruped, a hexapod and that of an octopod. These results seem to confirm that
as task learning difficulty increases the application of morphological
development to learning becomes more advantageous.Comment: 10 pages, 4 figure
Material properties affect evolution's ability to exploit morphological computation in growing soft-bodied creatures
The concept of morphological computation holds that the
body of an agent can, under certain circumstances, exploit
the interaction with the environment to achieve useful behavior,
potentially reducing the computational burden of
the brain/controller. The conditions under which such phenomenon
arises are, however, unclear. We hypothesize that
morphological computation will be facilitated by body plans
with appropriate geometric, material, and growth properties,
while it will be hindered by other body plans in which one or
more of these three properties is not well suited to the task.
We test this by evolving the geometries and growth processes
of soft robots, with either manually-set softer or stiffer material
properties. Results support our hypothesis: we find that
for the task investigated, evolved softer robots achieve better
performances with simpler growth processes than evolved
stiffer ones. We hold that the softer robots succeed because
they are better able to exploit morphological computation.
This four-way interaction among geometry, growth, material
properties and morphological computation is but one example
phenomenon that can be investigated using the system here
introduced, that could enable future studies on the evolution
and development of generic soft-bodied creatures
Biomimetic pneumatic soft actuator and microfluidic imaging system for analyzing nematodes locomotion
Hard-bodied robots’ operations always are limited because of their rigid structures. Recently, researchers has been inspired by some animals because they can exhibit complex movement with soft structure. Conventional manipulators operate difficultly because of their rigid links in some highly congested environments. They design soft robots, to replace traditional robots with rigid links. With a soft structure and large degrees of freedom, these robots can be used for tasks in highly congested environments. The elephant trunk is one of the most used models due to its high flexibility. Its shape can be changed when pressurized by air. Our study focuses on designing and fabricating a pneumatic soft actuator inspired by elephant trunk, and testing pneumatic actuations with a focus on achieving its multiple freedom of degree movement. Normally, Soft robots are always actuated by variable length tendons embedded in the soft segment. Compared to the traditional approach, pneumatic actuation does not damage the actuator because no more complex components need to be fabricated in the actuator.
Studying small model organisms such as Caenorhabditis elegans provides great opportunities for securing diseases in humans. C. elegans is easily grown in the laboratory, with maintained in agar-filled petri dishes. These small model organisms also have huge potential for use in drug delivery and image-based screening. There are many developments in microfluidic technologies for imaging small model organisms. Due to severe constraints of volume, Shadow-imaging is one of methods that can record the locomotion of nematodes. The microfluidic device is put on the top of the the camera chip, and the light source is put on the top of the device. Our study focuses on designing a microfluidic device to facilitate high-throughput, imaging-based screening of microscopic nematodes. It involves fabricating microfluidic device, designing and integrating siphon-based suction mechanism with multiple channels, and using the raspberry-pi camera to record the movement of nematodes in channels
An Experiment in Morphological Development for Learning ANN Based Controllers
Morphological development is part of the way any human or animal learns. The
learning processes starts with the morphology at birth and progresses through
changing morphologies until adulthood is reached. Biologically, this seems to
facilitate learning and make it more robust. However, when this approach is
transferred to robotic systems, the results found in the literature are
inconsistent: morphological development does not provide a learning advantage
in every case. In fact, it can lead to poorer results than when learning with a
fixed morphology. In this paper we analyze some of the issues involved by means
of a simple, but very informative experiment in quadruped walking. From the
results obtained an initial series of insights on when and under what
conditions to apply morphological development for learning are presented.Comment: 10 pages, 4 figures. arXiv admin note: text overlap with
arXiv:2003.0581
Beyond Body Shape and Brain: Evolving the Sensory Apparatus of Voxel-Based Soft Robots
Biological and artificial embodied agents behave by acquiring information through sensors, processing that information, and acting on the environment. The sensory apparatus, i.e., the location on the body of the sensors and the kind of information the sensors are able to capture, has a great impact on the agent ability of exhibiting complex behaviors. While in nature, the sensory apparatus is the result of a long-lasting evolution, in artificial agents (robots) it is usually the result of a design choice. However, when the agents are complex and the design space is large, making that choice can be hard. In this paper, we explore the possibility of evolving the sensory apparatus of voxel-based soft robots (VSRs), a kind of simulated robots composed of multiple deformable components. VSRs, due to their intrinsic modularity, allow for great freedom in how to shape the robot body, brain, and sensory apparatus. We consider a set of sensors that allow the agent to sense itself and the environment (using vision and touch) and we show, experimentally, that the effectiveness of the sensory apparatus depends on the shape of the body and on the actuation capability, i.e., the VSR strength. Then we show that evolutionary optimization is able to evolve an effective sensory apparatus, even when constraints on the availability of the sensors are posed. By extending the adaptation to the sensory apparatus, beyond the body shape and the brain, we believe that our study takes a step forward to the ambitious path towards self-building robots
Toward Shape Optimization of Soft Robots
International audienceIn this paper we present our work on shape optimization for soft robotics where the shape is optimized for a given soft robot usage. To obtain a parametric optimization with a reduced number of parameters, we rely on an approach where the designer progressively refines the parameter space and the fitness function until a satisfactory design is obtained. In our approach, we automatically generate FEM simulations of the soft robot and its environment to evaluate a fitness function while checking the consistency of the solution. Finally, we have coupled our framework to an evolutionary optimization algorithm, and demonstrated its use for optimizing the design of a deformable leg of a locomotive robot
Evolving soft locomotion in aquatic and terrestrial environments: effects of material properties and environmental transitions
Designing soft robots poses considerable challenges: automated design
approaches may be particularly appealing in this field, as they promise to
optimize complex multi-material machines with very little or no human
intervention. Evolutionary soft robotics is concerned with the application of
optimization algorithms inspired by natural evolution in order to let soft
robots (both morphologies and controllers) spontaneously evolve within
physically-realistic simulated environments, figuring out how to satisfy a set
of objectives defined by human designers. In this paper a powerful evolutionary
system is put in place in order to perform a broad investigation on the
free-form evolution of walking and swimming soft robots in different
environments. Three sets of experiments are reported, tackling different
aspects of the evolution of soft locomotion. The first two sets explore the
effects of different material properties on the evolution of terrestrial and
aquatic soft locomotion: particularly, we show how different materials lead to
the evolution of different morphologies, behaviors, and energy-performance
tradeoffs. It is found that within our simplified physics world stiffer robots
evolve more sophisticated and effective gaits and morphologies on land, while
softer ones tend to perform better in water. The third set of experiments
starts investigating the effect and potential benefits of major environmental
transitions (land - water) during evolution. Results provide interesting
morphological exaptation phenomena, and point out a potential asymmetry between
land-water and water-land transitions: while the first type of transition
appears to be detrimental, the second one seems to have some beneficial
effects.Comment: 37 pages, 22 figures, currently under review (journal
Outline of an evolutionary morphology generator towards the modular design of a biohybrid catheter
Biohybrid machines (BHMs) are an amalgam of actuators composed of living cells with synthetic materials. They are engineered in order to improve autonomy, adaptability and energy efficiency beyond what conventional robots can offer. However, designing these machines is no trivial task for humans, provided the field’s short history and, thus, the limited experience and expertise on designing and controlling similar entities, such as soft robots. To unveil the advantages of BHMs, we propose to overcome the hindrances of their design process by developing a modular modeling and simulation framework for the digital design of BHMs that incorporates Artificial Intelligence powered algorithms. Here, we present the initial workings of the first module in an exemplar framework, namely, an evolutionary morphology generator. As proof-of-principle for this project, we use the scenario of developing a biohybrid catheter as a medical device capable of arriving to hard-to-reach regions of the human body to release drugs. We study the automatically generated morphology of actuators that will enable the functionality of that catheter. The primary results presented here enforced the update of the methodology used, in order to better depict the problem under study, while also provided insights for the future versions of the software module