In recent years, swimming robots have been developed to achieve efficient propulsion and high maneuverability that are possessed naturally by fish. Previous studies have attempted to achieve swimming similar to fish by control based on physical models and top-down architectures, but have encountered problems due to the high complexity of the underwater environment. Several research works have tried to overcome these problems by exploiting embodiment—that is, by mimicking the physical properties of fish. To achieve more intelligent swimming from the perspective of the embodiment, we focused on a framework called physical reservoir computing (PRC). This framework allows us to utilize physical dynamics as a computational resource. In this study, we propose a soft sheet-like swimming robot and a PRC-based architecture that can be used to emulate swimming motions by exploit- ing its own body dynamics for closed-loop control. Through experiments, we demonstrated that our system satisfies the properties required for learning swimming motion through supervised learning. We also succeeded in robust motion gen- eration and environmental state estimation, opening up future prospects for more intelligent robot control and sensing.
When we attempt to define life, we tend to refer to individuals, those that are alive. But these individuals might be cells, organisms, colonies... ecosystems? We can describe living systems at different scales. Which ones might be the best ones to describe different selves? I explore this question using concepts from information theory, ALife, and Buddhist philosophy. After brief introductions, I review the implications of changing the scale of observation, and how this affects our understanding of selves at different structural, temporal, and informational scales. The conclusion is that there is no ``best'' scale for a self, as this will depend on the scale at which decisions must be made. Different decisions, different scales.
Animals that live in social groups must interact in order to stay together and move collectively. By socializing a robot with a group of weakly electric fish, we aim at answering fundamental biological questions about the rules that govern social interactions and cause group members to coordinate their movements and come to joint decisions. African weakly electric fish communicate at night by emitting and perceiving short electrical current pulses, a process called electrocommunication. Our experiments have shown that it is only possible to integrate a robotic agent into a group of electric fish if it emits electric signals and engages in electro-communication. All other sensory cues, like visual appearance, can be neglected. For full acceptance as a conspecific by live fish, the robot must be able to interact with the animals. We hypothesize that the integration of fish and robot into a mixed society can succeed when the robot's electric signaling interaction is matched by locomotor interactions that are congruent with the behavioral relevance of electro-communication.
—Conventional design for robotics is based on the assumption that the robot should operate only in one given environment. As a result, often their skills are not transferable. Biological systems on the other hand are surprisingly versatile and robust. They exhibit remarkable adaptivity by placing more emphasis on adapting their morphology. Consequently, providing robots with mechanisms to adapt their bodies (material properties and even removing/adding parts) could be a way to obtain more versatile and robust systems. In this paper we propose a novel method which uses genetic algorithms to evolve optimal adaptation rules for changing the bodies of soft robots. Instead of optimising the morphology directly, we optimise the rules that tell the robot how to adapt the body based on the feedback it receives when interacting with the environment. It uses a combination of local and global information to sculpt (i.e., change stiffness and remove body parts) the soft body to improve locomotion in different environments. We show that in some cases the same rule with the same starting morphology can lead to different, but beneficial morphologies in different environments, i.e., it can translate feedback from the different environments into different useful bodily changes. Furthermore, we demonstrate that some of the found rules are highly robust and are able to produce successful morphologies for a range of environments that haven’t been experienced during the optimisation process.
We evolve artificial agents to perform a simple tracking task in three conditions: one individual (Isolated Condition) and two joint action conditions with division of labor. The joint conditions differ by whether two agents switch complementary roles during the task (Generalist Condition) or always play the same role (Specialist Condition). At the end of evolutionary runs we calculate the agents’ neural complexity using Tononi-Sporns-Edelman (TSE) complexity measure which relates to Integrated Information Theory (IIT). We show that (1) division of labor with specialization leads to a level of neural complexity comparable to the complexity of perform- ing the same task alone, and that (2) both are lower than neural complexity when performing the task jointly with role switching. We further consider viewing collaborating agents as a single extended system and calculate its joint neural com- plexity. We demonstrate that contrary to our predictions, the same pattern of results, i.e., Generalists’ complexity being higher than Specialists’, holds also in this conceptualization.
