Search

The fabrication of thermo-magnetic dual-responsive soft robots often requires intricate designs to implement complex locomotion patterns and utilize the implemented responsive behaviors. This work demonstrates a minimally designed soft robot based on poly- N -isopropylacrylamide (pNIPAM) and ferromagnetic particles, showcasing excellent control over both thermo- and magnetic responses. Free radical polymerization enables the magnetic particles to be entrapped homogeneously within the polymeric network. The integration of magnetic shape programming and temperature response allows the robot to perform various tasks including shaping, locomotion, pick-and-place, and release maneuvers of objects using independent triggers. The robot can be immobilized in a gripping state through magnetic actuation, and a subsequent increase in temperature transitions the robot from a swollen to a collapsed state. The temperature switch enables the robot to maintain a secured configuration while executing other movements via magnetic actuation. This approach offers a straightforward yet effective solution for achieving full control over both stimuli in dual-responsive soft robotics.

In intertemporal choices, subjects face a trade-off between value and delay: achieving the most valuable outcome requires a longer time, whereas the immediately available option is objectively poorer. Intertemporal choices are ubiquitous, and comparative studies reveal commonalities and differences across species: all species devalue future rewards as a function of delay (delay aversion), yet there is a lot of inter-specific variance in how rapidly such devaluation occurs. These differences are often interpreted in terms of ecological rationality, as depending on environmental factors (e.g., feeding ecology) and the physiological and morphological constraints of different species (e.g., metabolic rate). Evolutionary hypotheses, however, are hard to verify in vivo, since it is difficult to observe precisely enough real environments, not to mention ancestral ones. In this paper, we discuss the viability of an approach based on evolutionary robotics: in Study 1, we evolve robots without a metabolism in five different ecologies; in Study 2, we evolve metabolic robots (i.e., robots that consume energy over time) in three different ecologies. The intertemporal choices of the robots are analyzed both in their ecology and under laboratory conditions. Results confirm the generality of delay aversion and the usefulness of studying intertemporal choice through experimental evolutionary robotics., (Copyright © 2015 Elsevier B.V. All rights reserved.)

Organisms that live in groups, from microbial symbionts to social insects and schooling fish, exhibit a number of highly efficient cooperative behaviors, often based on role taking and specialization. These behaviors are relevant not only for the biologist but also for the engineer interested in decentralized collective robotics. We address these phenomena by carrying out experiments with groups of two simulated robots controlled by neural networks whose connection weights are evolved by using genetic algorithms. These algorithms and controllers are well suited to autonomously find solutions for decentralized collective robotic tasks based on principles of self-organization. The article first presents a taxonomy of role-taking and specialization mechanisms related to evolved neural network controllers. Then it introduces two cooperation tasks, which can be accomplished by either role taking or specialization, and uses these tasks to compare four different genetic algorithms to evaluate their capacity to evolve a suitable behavioral strategy, which depends on the task demands. Interestingly, only one of the four algorithms, which appears to have more biological plausibility, is capable of evolving role taking or specialization when they are needed. The results are relevant for both collective robotics and biology, as they can provide useful hints on the different processes that can lead to the emergence of specialization in robots and organisms.

Distributed coordination of groups of individuals accomplishing a common task without leaders, with little communication, and on the basis of self-organizing principles, is an important research issue within the study of collective behavior of animals, humans, and robots. The article shows how distributed coordination allows a group of evolved, physically linked simulated robots (inspired by a robot under construction) to display a variety of highly coordinated basic behaviors such as collective motion, collective obstacle avoidance, and collective approach to light, and to integrate them in a coherent fashion. In this way the group is capable of searching and approaching a lighted target in an environment scattered with obstacles, furrows, and holes, where robots acting individually fail. The article shows how the emerged coordination of the group relies upon robust self-organizing principles (e.g., positive feedback) based on a novel sensor that allows the single robots to perceive the group's "average" motion direction. The article also presents a robust solution to a difficult coordination problem, which might also be encountered by some organisms, caused by the fact that the robots have to be capable of moving in any direction while being physically connected. Finally, the article shows how the evolved distributed coordination mechanisms scale very well with respect to the number of robots, the way in which robots are assembled, the structure of the environment, and several other aspects.

We present a set of experiments in which simulated robots are evolved for the ability to aggregate and move together toward a light target. By developing and using quantitative indexes that capture the structural properties of the emerged formations, we show that evolved individuals display interesting behavioral patterns in which groups of robots act as a single unit. Moreover, evolved groups of robots with identical controllers display primitive forms of situated specialization and play different behavioral functions within the group according to the circumstances. Overall, the results presented in the article demonstrate that evolutionary techniques, by exploiting the self-organizing behavioral properties that emerge from the interactions between the robots and between the robots and the environment, are a powerful method for synthesizing collective behavior.

The evolution of simulated robots with three different architectures is studied in this article. We compare a nonmodular feed-forward network, a hardwired modular, and a duplication-based modular motor control network. We conclude that both modular architectures outperform the non-modular architecture, both in terms of rate of adaptation as well as the level of adaptation achieved. The main difference between the hardwired and duplication-based modular architectures is that in the latter the modules reached a much higher degree of functional specialization of their motor control units with regard to high-level behavioral functions. The hardwired architectures reach the same level of performance, but have a more distributed assignment of functional tasks to the motor control units. We conclude that the mechanism through which functional specialization is achieved is similar to the mechanism proposed for the evolution of duplicated genes. It is found that the duplication of multifunctional modules first leads to a change in the regulation of the module, leading to a differentiation of the functional context in which the module is used. Then the module adapts to the new functional context. After this second step the system is locked into a functionally specialized state. We suggest that functional specialization may be an evolutionary absorption state.

The evolution of simulated robots with three different architectures is studied. We compared a non-modular feed forward network, a hardwired modular and a duplication-based modular motor control network. We conclude that both modular architectures outperform the non-modular architecture, both in terms of rate of adaptation as well as the level of adaptation achieved. The main difference between the hardwired and duplication-based modular architectures is that in the latter the modules reached a much higher degree of functional specialization of their motor control units with regard to high level behavioral functions. The hardwired architectures reach the same level of performance, but have a more distributed assignment of functional tasks to the motor control units. We conclude that the mechanism through which functional specialization is achieved is similar to the mechanism proposed for the evolution of duplicated genes. It is found that the duplication of multifunctional modules first leads to a change in the regulation of the module, leading to a differentiation of the functional context in which the module is used. Then the module adapts to the new functional context. After this second step the system is locked into a functionally specialized state. We suggest that functional specialization may be an evolutionary absorption state.