5,400 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
Quantifying the Evolutionary Self Structuring of Embodied Cognitive Networks
We outline a possible theoretical framework for the quantitative modeling of
networked embodied cognitive systems. We notice that: 1) information self
structuring through sensory-motor coordination does not deterministically occur
in Rn vector space, a generic multivariable space, but in SE(3), the group
structure of the possible motions of a body in space; 2) it happens in a
stochastic open ended environment. These observations may simplify, at the
price of a certain abstraction, the modeling and the design of self
organization processes based on the maximization of some informational
measures, such as mutual information. Furthermore, by providing closed form or
computationally lighter algorithms, it may significantly reduce the
computational burden of their implementation. We propose a modeling framework
which aims to give new tools for the design of networks of new artificial self
organizing, embodied and intelligent agents and the reverse engineering of
natural ones. At this point, it represents much a theoretical conjecture and it
has still to be experimentally verified whether this model will be useful in
practice.
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
Soft Scalable Self-Reconfigurable Modular Cellbot
Hazardous environments such as disaster affected areas, outer space, and radiation affected areas are dangerous for humans. Autonomous systems which can navigate through these environments would reduce risk of life. The terrains in these applications are diverse and unknown, hence there is a requirement for a robot which can self-adapt its morphology and use suitable control to optimally move in the desired manner. Although there exist monolithic robots for some of these applications, such as the Curiosity rover for Mars exploration, a modular robot containing multiple simple units could increase the fault tolerance. A modular design also enables scaling up or down of the robot based on the current task, for example, scaling up by connecting multiple units to cover a wider area or scaling down to pass through a tight space.Taking bio-inspiration from cells, where â based on environmental conditions â cells come together to form different structures to carry out different tasks, a soft modular robot called Cellbot was developed which was composed of multiple units called âcellsâ. Tests were conducted to understand the cellbot movement over different frictional surfaces for different actuation functions, the number of cells connected in a line (1D), and the shapes formed by connecting cells in 2D. A simulation model was developed to test a large range of frictional values and actuation functions for different friction coefficients. Based on the obtained results, cells could be designed using a material with frictional properties lying in the optimal locomotion range. In other cases, where the application has diverse terrains, the number of connected units can be changed to optimise the robot locomotion. Initial tests were conducted using a âball robotâ, where the cellbot was designed using balls which touch ground to exploit friction and actuators to provide force to move the robot. The model was extended to develop, a âbellow robotâ which was fabricated using hyper-elastic bellows and employed pneumatic actuation. The amount of inflation of a cell and its neighbouring cells determined if the cell would touch the ground or be lifted up. This was used to change cell behaviour where a cell could be touching ground to provide anchoring friction, or lifted to push or pull the cells and thereby move the robot. The cells were connected by magnets which could be disconnected and reconnected by morphing the robot body. The cellbot can thus reconfigure by changing the number of connected units or its shape. The easy detachment can be used to remove and replace damaged cells. Complex cellbot movements can be achieved by either switching between different robot morphologies or by changing actuation control.Future cellbots will be controlled remotely to change their morphology, control, and number of connected cells, making them suitable for missions which require fault tolerance and autonomous shape adaptation. The proposed cellbot platform has the potential to reduce the energy, time and costs in comparison to traditional robots and has potential for applications such as exploration missions for outer space, search and rescue missions for disaster affected areas, internal medical procedures, and nuclear decommissioning.<br/
Factors Impacting Diversity and Effectiveness of Evolved Modular Robots
In many natural environments, different forms of living organisms successfully accomplish the same task while being diverse in shape and behavior. This biodiversity is what made life capable of adapting to disrupting changes. Being able to reproduce biodiversity in artificial agents, while still optimizing them for a particular task, might increase their applicability to scenarios where human response to unexpected changes is not possible. In this work, we focus on Voxel-based Soft Robots (VSRs), a form of robots that grants great freedom in the design of both morphology and controller and is hence promising in terms of biodiversity. We use evolutionary computation for optimizing, at the same time, morphology and controller of VSRs for the task of locomotion. We investigate experimentally whether three key factorsârepresentation, Evolutionary Algorithm (EA), and environmentâimpact the emergence of biodiversity and if this occurs at the expense of effectiveness. We devise an automatic machine learning pipeline for systematically characterizing the morphology and behavior of robots resulting from the optimization process. We classify the robots into species and then measure biodiversity in populations of robots evolved in a multitude of conditions resulting from the combination of different morphology representations, controller representations, EAs, and environments. The experimental results suggest that, in general, EA and environment matter more than representation. We also propose a novel EA based on a speciation mechanism that operates on morphology and behavior descriptors and we show that it allows to jointly evolve morphology and controller of effective and diverse VSRs
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
How the Morphology Encoding Influences the Learning Ability in Body-Brain Co-Optimization
Embedding the learning of controllers within the evolution of morphologies has emerged as an effective strategy for the co-optimization of agents' bodies and brains. Intuitively, that is how nature shaped animal life on Earth. Still, the design of such co-optimization is a complex endeavor; one issue is the choice of the genetic encoding for the morphology. Such choice can be crucial for the effectiveness of learning, i.e., how fast and to what degree agents adapt, through learning, during their life. Here we evolve the morphologies of voxel-based soft agents with two different encodings, direct and indirect while learning the controllers with reinforcement learning. We experiment with three tasks, ranging from cave crawling to beam toppling, and study how the encoding influences the learning outcome. Our results show that the direct encoding corresponds to increased ability to learn, mostly in terms of learning speed. The same is not always true for the indirect one. We link these results to different shades of the Baldwin effect, consisting of morphologies being selected for increasing an agentâs ability to learn during its lifetime
Osnovni tjelesni nacrti za mekane modularne pneubotske konstrukcije u arhitekturi
This article introduces a theoretical model for the design of pneubotic structures that can be constructed and actuated by using the modular unit volume element. Through analysis of construction of soft robots, pneumatically adaptive and responsive structures and art installations, a set of four basic body plans is proposed, as abstract expressions that form a base for the design of soft modular pneubotics in architecture.Älanak donosi teorijski model za projektiranje mekih pneubotskih konstrukcija koje se mogu konstruirati i pokretati koristeÄi jediniÄni modularni volumenski element. Analizom konstrukcije mekih robota, pneumatski prilagodljivih i reagirajuÄih konstrukcija te umjetniÄkih instalacija, dobiven je set od Äetiriju osnovnih tipova tjelesnih nacrta kao apstraktnih izraza za projektiranje sloĆŸenih modularnih pneubotskih konstrukcija u arhitekturi
Morphology Choice Affects the Evolution of Affordance Detection in Robots
A vital component of intelligent action is affordance detection: understanding what actions external objects afford the viewer. This requires the agent to understand the physical nature of the object being viewed, its own physical nature, and the potential relationships possible when they interact. Although robotics researchers have investigated affordance detection, the way in which the morphology of the robot facilitates, obstructs, or otherwise influences the robotâs ability to detect affordances has yet to be studied. We do so here and find that a robot with an appropriate morphology can evolve to predict whether it will fit through an aperture with just minimal tactile feedback. We also find that some robot morphologies facilitate the evolution of more accurate affordance detection, while others do not if all have the same evolutionary optimization budget. This work demonstrates that sensation, thought, and action are necessary but not sufficient for understanding how affordance detection may evolve in organisms or robots: morphology must also be taken into account. It also suggests that, in the future, we may optimize morphology along with control in order to facilitate affordance detection in robots, and thus improve their reliable and safe action in the world
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